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Yu Tian

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39 papers
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39

AAAI Conference 2026 Conference Paper

DeepWriter: A Multi-Agent Collaboration Framework for Information-rich Ultra-long Book Writing

  • Ming Wang
  • Minghao Hu
  • Xiuli Kang
  • Li He
  • Yu Tian
  • Chunming Liu
  • Han Shi
  • Zhunchen Luo

Long-form books are among the most information-rich and structurally complex forms of written content, often exceeding 100,000 words. While recent methods have enabled basic long-text generation, they remain limited in two key aspects: the inability to generate ultra-long content at book scale, and the lack of mechanisms for integrating rich factual information. To address these limitations, we propose DeepWriter, a multi-agent collaborative framework that follows a structured planning-then-generation paradigm. It first constructs a detailed book outline with narrative arcs and chapter semantics, then incrementally generates content conditioned on retrieved knowledge and contextual signals. DeepWriter supports controllable generation of full-length books exceeding 100,000 words, enriched with citations, trivia and images. To support evaluation beyond surface-level fluency, we introduce DeepWriter-Bench, a bilingual benchmark of 18 annotated books designed to assess book-scale coherence, richness, and factual grounding. Additionally, we propose BookScore, a unified 100-point metric for quantifying book maturity. Experimental results show that DeepWriter achieves a state-of-the-art BookScore of 80.92, consistently outperforming strong baselines.

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AIIM Journal 2026 Journal Article

Hierarchical classification for differential diagnosis of fever of unknown origin: A multi-task learning approach with self-adaptive representation sharing

  • Zhixiao Wang
  • Yu Tian
  • Jian Liu
  • Tianshu Zhou
  • Yunqing Qiu
  • Jingsong Li

Leveraging label dependencies as prior knowledge during both training and testing has proven valuable across diverse domains such as image annotation and text categorization. In our previous research, we successfully reframed the clinical challenge of aiding decision-making for patients with fever of unknown origin (FUO) as a hierarchical classification problem, validating its feasibility through local methods. However, these approaches still encounter challenges, including high training costs and potential error propagation during predictions. Moreover, existing global approaches for exploiting label dependencies impose strict prerequisites—such as fixed data modalities, manual specification of information-sharing directions, and equal-length label sequences—that limit their applicability to FUO etiologies. In this paper, we introduce a novel global hierarchical classification method based on a multi-task learning architecture for the early diagnosis of FUO patients. Our method leverages multimodal clinical data and a predefined label hierarchy and comprises three key components: a task decomposition strategy employing End-of-Sequence (EOS) markers (with each parent node in the label hierarchy corresponding to an individual classification task), a multimodal data feature extraction and fusion module, and a self-adaptive representation sharing module (Sa-RSM). We evaluated our approach on an experimental dataset extracted from electronic health records (EHRs) of a large-scale tertiary hospital in China, spanning January 2011 to October 2020 and comprising 34, 051 hospital admissions of 30, 794 FUO patients. Our results clearly demonstrate that the proposed method not only achieves superior predictive performance but also proactively halts predictions at coarser-grained classification tasks. Moreover, even in cases of misclassification, our method exhibits lower mistake severity, underscoring its potential clinical utility.

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AAAI Conference 2026 Conference Paper

MultiMedBench: A Scenario-Aware Benchmark for Evaluating Knowledge Editing in Medical VQA

  • Shengtao Wen
  • Haodong Chen
  • Yadong Wang
  • Zhongying Pan
  • Xiang Chen
  • Yu Tian
  • Bo Qian
  • Dong Liang

Knowledge editing (KE) provides a scalable approach for updating factual knowledge in large language models without full retraining. While previous studies have demonstrated effectiveness in general domains and medical QA tasks, little attention has been paid to KE in multimodal medical scenarios. Unlike text-only settings, medical KE demands integrating updated knowledge with visual reasoning to support safe and interpretable clinical decisions. To address this gap, we propose MultiMedBench, the first benchmark tailored to evaluating KE in clinical multimodal tasks. Our framework spans both understanding and reasoning task types, defines a three-dimensional metric suite (reliability, generality, and locality), and supports cross-paradigm comparisons across general and domain-specific models. We conduct extensive experiments under single-editing and lifelong-editing settings. Results suggest that current methods struggle with generalization and long-tail reasoning, particularly in complex clinical workflows. We further present an efficiency analysis (e.g., edit latency, memory footprint), revealing practical trade-offs in real-world deployment across KE paradigms. Overall, MultiMedBench not only reveals the limitations of current approaches but also provides a solid foundation for developing clinically robust knowledge editing techniques in the future.

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AAAI Conference 2026 Conference Paper

Rethinking the Reliability of Multi-agent System: A Perspective from Byzantine Fault Tolerance

  • Lifan Zheng
  • Jiawei Chen
  • Qinghong Yin
  • Jingyuan Zhang
  • Xinyi Zeng
  • Yu Tian

Ensuring the reliability of agent architectures and effectively identifying problematic agents when failures occur are crucial challenges in multi-agent systems (MAS). Advances in large language models (LLMs) have established LLM-based agents as a major branch of MAS, enabling major breakthroughs in complex problem solving and world modeling. However, the reliability implications of this shift remain largely unexplored. i.e., whether substituting traditional agents with LLM-based agents can effectively enhance the reliability of MAS. In this work, we investigate and quantify the reliability of LLM-based agents from the perspective of Byzantine fault tolerance. We observe that LLM-based agents demonstrate stronger skepticism when processing erroneous message flows, a characteristic that enables them to outperform traditional agents across different topological structures. Motivated by the results of the pilot experiment, we design CP-WBFT, a confidence probe-based weighted Byzantine Fault Tolerant consensus mechanism to enhance the stability of MAS with different topologies. It capitalizes on the intrinsic reflective and discriminative capabilities of LLMs by employing a probe-based, weighted information flow transmission method to improve the reliability of LLM-based agents. Extensive experiments demonstrate that CP-WBFT achieves superior performance across diverse network topologies under extreme Byzantine conditions (85.7 % fault rate). Notably, our approach surpasses traditional methods by attaining remarkable accuracy on various topologies and maintaining strong reliability in both mathematical reasoning and safety assessment tasks.

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EAAI Journal 2025 Journal Article

Adaptive Deformable Convolutional Neural Network Framework for depression-related behavioral analysis in mice

  • Jian Li
  • Ziyi Li
  • Peng Shan
  • Xiaoyong Lyu
  • Yu Tian
  • Chen Du
  • Ying Wang
  • Yuliang Zhao

The use of approximately 1 billion laboratory animals annually in research highlights the urgent need for advanced methods to analyze behavioral dynamics, particularly in mice. Capturing subtle and prolonged behavioral changes, such as those observed in long-term depression studies, poses a significant challenge. To address this, we propose an Adaptive Deformable Convolutional Neural Network Framework for depression-related behavioral analysis in mice. By integrating DeepLabCut (DLC) with deformable convolutional networks (DCN) and convolutional block attention module (CBAM), the framework captures subtle and prolonged behavioral changes with high precision. Adaptive image deformation encodes joint movements into image representations, enabling robust analysis of spatial and temporal patterns. In depression modeling experiment, the framework achieved over 80% classification accuracy, demonstrating its scalability and efficiency. This non-invasive, automated solution represents a transformative advancement in behavioral analysis, offering a reliable tool for long-term studies in animal models.

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AAAI Conference 2025 Conference Paper

AI-generated Image Quality Assessment in Visual Communication

  • Yu Tian
  • Yixuan Li
  • Baoliang Chen
  • Hanwei Zhu
  • Shiqi Wang
  • Sam Kwong

Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality assessment (IQA) algorithms primarily focus on low-level visual perception, while existing IQA works on AIGIs overemphasize the generated content itself, neglecting its effectiveness in real-world applications. To bridge this gap, we propose AIGI-VC, a quality assessment database for AI-Generated Images in Visual Communication, which studies the communicability of AIGIs in the advertising field from the perspectives of information clarity and emotional interaction. The dataset consists of 2,500 images spanning 14 advertisement topics and 8 emotion types. It provides coarse-grained human preference annotations and fine-grained preference descriptions, benchmarking the abilities of IQA methods in preference prediction, interpretation, and reasoning. We conduct an empirical study of existing representative IQA methods and large multi-modal models on the AIGI-VC dataset, uncovering their strengths and weaknesses.

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JBHI Journal 2025 Journal Article

Benchmarking Large Language Models in Evidence-Based Medicine

  • Jin Li
  • Yiyan Deng
  • Qi Sun
  • Junjie Zhu
  • Yu Tian
  • Jingsong Li
  • Tingting Zhu

Evidence-based medicine (EBM) represents a paradigm of providing patient care grounded in the most current and rigorously evaluated research. Recent advances in large language models (LLMs) offer a potential solution to transform EBM by automating labor-intensive tasks and thereby improving the efficiency of clinical decision-making. This study explores integrating LLMs into the key stages in EBM, evaluating their ability across evidence retrieval (PICO extraction, biomedical question answering), synthesis (summarizing randomized controlled trials), and dissemination (medical text simplification). We conducted a comparative analysis of seven LLMs, including both proprietary and open-source models, as well as those fine-tuned on medical corpora. Specifically, we benchmarked the performance of various LLMs on each EBM task under zero-shot settings as baselines, and employed prompting techniques, including in-context learning, chain-of-thought reasoning, and knowledge-guided prompting to enhance their capabilities. Our extensive experiments revealed the strengths of LLMs, such as remarkable understanding capabilities even in zero-shot settings, strong summarization skills, and effective knowledge transfer via prompting. Promoting strategies such as knowledge-guided prompting proved highly effective (e. g. , improving the performance of GPT-4 by 13. 10% over zero-shot in PICO extraction). However, the experiments also showed limitations, with LLM performance falling well below state-of-the-art baselines like PubMedBERT in handling named entity recognition tasks. Moreover, human evaluation revealed persisting challenges with factual inconsistencies and domain inaccuracies, underscoring the need for rigorous quality control before clinical application. This study provides insights into enhancing EBM using LLMs while highlighting critical areas for further research.

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JMLR Journal 2025 Journal Article

Curvature-based Clustering on Graphs

  • Yu Tian
  • Zachary Lubberts
  • Melanie Weber

Unsupervised node clustering (or community detection) is a classical graph learning task. In this paper, we study algorithms that exploit the geometry of the graph to identify densely connected substructures, which form clusters or communities. Our method implements discrete Ricci curvatures and their associated geometric flows, under which the edge weights of the graph evolve to reveal its community structure. We consider several discrete curvature notions and analyze the utility of the resulting algorithms. In contrast to prior literature, we study not only single-membership community detection, where each node belongs to exactly one community, but also mixed-membership community detection, where communities may overlap. For the latter, we argue that it is beneficial to perform community detection on the line graph, i.e., the graph's dual. We provide both theoretical and empirical evidence for the utility of our curvature-based clustering algorithms. In addition, we give several results on the relationship between the curvature of a graph and that of its dual, which enable the efficient implementation of our proposed mixed-membership community detection approach and which may be of independent interest for curvature-based network analysis. [abs] [ pdf ][ bib ] &copy JMLR 2025. ( edit, beta )

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JBHI Journal 2025 Journal Article

Dynamically Enhanced Multi-organ Segmentation Base on Boundary-Aware Partial Label

  • Yanxia Zhao
  • Peijun Hu
  • Yu Tian
  • Tianshu Zhou
  • Yan Zhu
  • Jingsong Li

Accurate multi-organ segmentation of abdominal CT is essential for many clinical applications, yet it often relies on large, fully annotated datasets. However, most available datasets are partially labeled, collected from different medical centers. To address this, we propose BAPLDE-MOSNet, a boundary-aware multi-organ segmentation network that leverages task-guided attention and dynamic feature enhancement modules to handle partially labeled data. BAPLDE-MOSNet integrates an edge prediction auxiliary regression network into the basic segmentation architecture in a multi-task learning manner. In addition, It introduces a boundary correction module by embedding boundary-related edge features into the segmentation task-related feature representation to effectively utilize boundary information to guide more accurate localization and segmentation of abdominal multi-organs. Moreover, a dynamic feature enhancement module is introduced to improve the network's attention to the target area. Our proposed method is rigorously validated on five public datasets (LiTS, KiTS, MSD Pancreas, MSD Spleen and the external BTCV benchmark), achieving state-of-the-art performance with an average DSC of 93. 42% and HD95 of 3. 635mm. Notably, it exhibits superior generalization on the external BTCV dataset (average DSC of 77. 87% and average HD95 of 26. 626 mm), outperforming both specialized single-organ networks and existing multi-organ approaches in comprehensive evaluations.

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ICRA Conference 2025 Conference Paper

Improving Efficiency in Path Planning: Tangent Line Decomposition Algorithm

  • Yu Tian
  • Hongliang Ren 0001

This paper introduces a tangent line decomposition (TLD) algorithm that efficiently finds collision-free paths close to optimal in both 2D and 3D environments. Compared with the existing visibility line-based algorithms, the proposed algorithm innovatively proposed the concept of tangent line decomposition, which decomposes complicated planning into many simple steps. For each step, only one key obstacle is taken into consideration. Besides, instead of constructing a complete graph, a best-first search algorithm is used to avoid searching redundant edges. The path planned by the algorithm is not the optimal path. However, following the idea of the informed RRT* algorithm, the path length planned by TLD can be used as a precondition for other optimal algorithms. In this way, the overall efficiency can be significantly improved. The simulations show that the proposed methods outperform existing methods regarding planning efficiency and solution quality.

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AAAI Conference 2025 Conference Paper

Incomplete Modality Disentangled Representation for Ophthalmic Disease Grading and Diagnosis

  • Chengzhi Liu
  • Zile Huang
  • Zhe Chen
  • Feilong Tang
  • Yu Tian
  • Zhongxing Xu
  • Zihong Luo
  • Yalin Zheng

Ophthalmologists typically require multimodal data sources to improve diagnostic accuracy in clinical decisions. However, due to medical device shortages, low-quality data and data privacy concerns, missing data modalities are common in real-world scenarios. Existing deep learning methods tend to address it by learning an implicit latent subspace representation for different modality combinations. We identify two significant limitations of these methods: (1) implicit representation constraints that hinder the model's ability to capture modality-specific information and (2) modality heterogeneity, causing distribution gaps and redundancy in feature representations. To address these, we propose an Incomplete Modality Disentangled Representation (IMDR) strategy, which disentangles features into explicit independent modal-common and modal-specific features by guidance of mutual information, distilling informative knowledge and enabling it to reconstruct valuable missing semantics and produce robust multimodal representations. Furthermore, we introduce a joint proxy learning module that assists IMDR in eliminating intra-modality redundancy by exploiting the extracted proxies from each class. Experiments on four ophthalmology multimodal datasets demonstrate that the proposed IMDR outperforms the state-of-the-art methods significantly.

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IROS Conference 2025 Conference Paper

Jacobian Exploratory Dual-Phase Reinforcement Learning for Dynamic Endoluminal Navigation of Deformable Continuum Robots

  • Yu Tian
  • Chi Kit Ng
  • Hongliang Ren 0001

Deformable continuum robots (DCRs) present unique planning challenges due to nonlinear deformation mechanics and partial state observability, violating the Markov assumptions of conventional reinforcement learning (RL) methods. While Jacobian-Based approaches offer theoretical foundations for rigid manipulators, their direct application to DCRs remains limited by time-varying kinematics and underactuated deformation dynamics. This paper proposes Jacobian Exploratory Dual-Phase RL (JEDP-RL), a framework that decomposes planning into phased Jacobian estimation and policy execution. During each training step, we first perform small-scale local exploratory actions to estimate the deformation Jacobian matrix, then augment the state representation with Jacobian features to restore approximate Markovianity. Extensive SOFA surgical dynamic simulations demonstrate JEDP-RL’s three key advantages over proximal policy optimization (PPO) baselines: 1) Convergence speed: 3. 2× faster policy convergence, 2) Navigation efficiency: requires 25% fewer steps to reach the target, and 3) Generalization ability: achieve 92% success rate under material property variations and achieve 83% (33% higher than PPO) success rate in the unseen tissue environment.

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IROS Conference 2025 Conference Paper

Learning to Perform Low-Contact Autonomous Nasotracheal Intubation by Recurrent Action-Confidence Chunking with Transformer

  • Yu Tian
  • Ruoyi Hao
  • Yiming Huang 0007
  • Dihong Xie
  • Catherine Po Ling Chan
  • Jason Ying-Kuen Chan
  • Hongliang Ren 0001

Nasotracheal intubation (NTI) is critical for establishing artificial airways in clinical anesthesia and critical care. Current manual methods face significant challenges, including cross-infection, especially during respiratory infection care, and insufficient control of endoluminal contact forces, increasing the risk of mucosal injuries. While existing studies have focused on automated endoscopic insertion, the automation of NTI remains unexplored despite its unique challenges: Nasotracheal tubes exhibit greater diameter and rigidity than standard endoscopes, substantially increasing insertion complexity and patient risks. We propose a novel autonomous NTI system with two key components to address these challenges. First, an autonomous NTI system is developed, incorporating a prosthesis embedded with force sensors, allowing for safety assessment and data filtering. Then, the Recurrent Action-Confidence Chunking with Transformer (RACCT) model is developed to handle complex tube-tissue interactions and partial visual observations. Experimental results demonstrate that the RACCT model outperforms the ACT model in all aspects and achieves a 66% reduction in average peak insertion force compared to manual operations while maintaining equivalent success rates. This validates the system’s potential for reducing infection risks and improving procedural safety.

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JBHI Journal 2025 Journal Article

pDenoiser: A Personalized Speech Enhancement Neural Network for Pre-Hospital Emergency Medical Services

  • Zhenchuan Zhang
  • Yu Tian
  • Tianshu Zhou
  • Yinghao Zhao
  • Jinpeng Li
  • Jungen Zhang
  • Jingsong Li

Pre-hospital emergency medical service (EMS) tasks often come with complex and diverse noise interferences, posing challenges in implementing ASR-based medical technologies and hindering efficient and accurate telephonic communication. Among the different types of noise distortion, interfering speech is especially annoying. To address these issues, our aim is to develop a technology capable of extracting the intended speech content of the target physician from noisy and mixed audio during EMS tasks. In this work, we propose a monoaural personalized speech enhancement (PSE) method called pDenoiser, which is a real-time neural network that operates in the time domain. By leveraging the prior vocalization cues of emergency physicians, pDenoiser selectively enhances target speech components while suppressing noise and nontarget speech components, thereby improving speech quality and speech recognition accuracy under noisy conditions. We demonstrate the potential value of our approach through evaluations on both public general- domain test sets and our self-collected real-world EMS test sets. The experimental results are promising, as our model effectively promotes both speech quality and ASR performance under various conditions and outperforms related methods across multiple evaluation metrics. Our methodology will hopefully elevate EMS efficiency and fortify security against nontarget speech during EMS tasks.

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ICRA Conference 2025 Conference Paper

Variable-Stiffness Nasotracheal Intubation Robot with Passive Buffering: A Modular Platform in Mannequin Studies

  • Ruoyi Hao
  • Jiewen Lai
  • Wenqi Zhong
  • Dihong Xie
  • Yu Tian
  • Tao Zhang
  • Yang Zhang 0053
  • Catherine Po Ling Chan

Intubation is a critical medical procedure for securing airway patency in patients, but the inconsistent skill levels among medical practitioners necessitate the advancement of better robotic solutions. While orotracheal intubation robots have been widely developed, nasotracheal intubation remains essential in specific clinical scenarios. However, nasotracheal intubation robots are still underdeveloped and lack buffer protection mechanisms to ensure safety. This study presents a novel variable-stiffness nasotracheal intubation robot (NIR) with passive buffering. The proposed NIR is a modular platform capable of performing the main steps of nasotracheal intubation, validated through mannequin studies via teleoperation. We proposed a variable-stiffness fiberoptic bronchoscope (FOB) control module for the FOB distal end control, and validated its dual functionality in experiments: low-stiffness mode provides passive buffering during nasal cavity navigation, with a frontal peak force of 2. 8 N and a lateral peak force of 0. 12 N; high-stiffness mode enhances load-bearing capacity for near-glottis navigation, with a frontal bearing force of 4. 9 N and a lateral bearing force of 0. 42 N. Additionally, a compact ( $\mathbf{74} \times \mathbf{64}\times \mathbf{53}$ mm, 150 g) FOB feeding module with passive failure protection was designed to limit the max frontal impact force to 2. 3 N.

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AIIM Journal 2024 Journal Article

A few-shot disease diagnosis decision making model based on meta-learning for general practice

  • Qianghua Liu
  • Yu Tian
  • Tianshu Zhou
  • Kewei Lyu
  • Ran Xin
  • Yong Shang
  • Ying Liu
  • Jingjing Ren

Background Diagnostic errors have become the biggest threat to the safety of patients in primary health care. General practitioners, as the “gatekeepers” of primary health care, have a responsibility to accurately diagnose patients. However, many general practitioners have insufficient knowledge and clinical experience in some diseases. Clinical decision making tools need to be developed to effectively improve the diagnostic process in primary health care. The long-tailed class distributions of medical datasets are challenging for many popular decision making models based on deep learning, which have difficulty predicting few-shot diseases. Meta-learning is a new strategy for solving few-shot problems. Methods and materials In this study, a few-shot disease diagnosis decision making model based on a model-agnostic meta-learning algorithm (FSDD-MAML) is proposed. The MAML algorithm is applied in a knowledge graph-based disease diagnosis model to find the optimal model parameters. Moreover, FSDD-MAML can learn learning rates for all modules of the knowledge graph-based disease diagnosis model. For n -way, k -shot learning tasks, the inner loop of FSDD-MAML performs multiple gradient update steps to learn internal features in disease classification tasks using n × k examples, and the outer loop of FSDD-MAML optimizes the meta-objective to find the associated optimal parameters and learning rates. FSDD-MAML is compared with the original knowledge graph-based disease diagnosis model and other meta-learning algorithms based on an abdominal disease dataset. Result Meta-learning algorithms can greatly improve the performance of models in top-1 evaluation compared with top-3, top-5, and top-10 evaluations. The proposed decision making model FSDD-MAML outperforms all the other models, with a precision@1 of 90. 02 %. We achieve state-of-the-art performance in the diagnosis of all diseases, and the prediction performance for few-shot diseases is greatly improved. For the two groups with the fewest examples of diseases, FSDD-MAML achieves relative increases in precision@1 of 29. 13 % and 21. 63 % compared with the original knowledge graph-based disease diagnosis model. In addition, we analyze the reasoning process of several few-shot disease predictions and provide an explanation for the results. Conclusion The decision making model based on meta-learning proposed in this paper can support the rapid diagnosis of diseases in general practice and is especially capable of helping general practitioners diagnose few-shot diseases. This study is of profound significance for the exploration and application of meta-learning to few-shot disease assessment in general practice.

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EAAI Journal 2024 Journal Article

A model-free toolface control strategy for cross-well intelligent directional drilling

  • Jiasheng Hao
  • Qingtong You
  • Zhinan Peng
  • Dongwei Ma
  • Yu Tian

Toolface adjustment methods for directional drilling in oil and gas drilling currently rely heavily on manual real-time interventions for continuous adjustment. However, due to the influence of manual experience, these existing methods suffer from unstable effects and high labor costs. As energy consumption continues to rise, the demand for intelligent directional drilling is becoming increasingly pressing. To address the challenges posed by automatic adjustment issues encountered in actual drilling operations under various complex downhole environments, this study presents a model-free online learning adaptive decision strategy for cross-well intelligent adjustment and toolface stabilization, which is applicable for both slide and rotatory steerable systems. A reward function embedded with expert operating experience is developed to learn the orientation strategy from the driller's corrective actions. Additionally, a priority-based experience replay mechanism is introduced to enhance online learning efficiency. To accurately simulate the directional drilling process and pre-train the orientation strategy, a data-driven directional drilling simulation environment is proposed. With the aim of facilitating implementation and widespread adoption in practical engineering, this study also involves the migration of a strategic model, integration of a real-time interaction module, and encapsulation of algorithms for field applications. Simulations and field experiments are conducted to validate the effectiveness of the proposed strategy. The experimental results demonstrate that the strategy can achieve decision-making goals in a short period of time.

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JBHI Journal 2024 Journal Article

An Explainable and Personalized Cognitive Reasoning Model Based on Knowledge Graph: Toward Decision Making for General Practice

  • Qianghua Liu
  • Yu Tian
  • Tianshu Zhou
  • Kewei Lyu
  • Zhixiao Wang
  • Yixiao Zheng
  • Ying Liu
  • Jingjing Ren

General practice plays a prominent role in primary health care (PHC). However, evidence has shown that the quality of PHC is still unsatisfactory, and the accuracy of clinical diagnosis and treatment must be improved in China. Decision making tools based on artificial intelligence can help general practitioners diagnose diseases, but most existing research is not sufficiently scalable and explainable. An explainable and personalized cognitive reasoning model based on knowledge graph (CRKG) proposed in this article can provide personalized diagnosis, perform decision making in general practice, and simulate the mode of thinking of human beings utilizing patients’ electronic health records (EHRs) and knowledge graph. Taking abdominal diseases as the application point, an abdominal disease knowledge graph is first constructed in a semiautomated manner. Then, the CRKG designed referring to dual process theory in cognitive science involves the update strategy of global graph representations and reasoning on a personal cognitive graph by adopting the idea of graph neural networks and attention mechanisms. For the diagnosis of diseases in general practice, the CRKG outperforms all the baselines with a precision@1 of 0. 7873, recall@10 of 0. 9020 and hits@10 of 0. 9340. Additionally, the visualization of the reasoning process for each visit of a patient based on the knowledge graph enhances clinicians' comprehension and contributes to explainability. This study is of great importance for the exploration and application of decision making based on EHRs and knowledge graph.

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ICRA Conference 2024 Conference Paper

Safe Table Tennis Swing Stroke with Low-Cost Hardware

  • Francesco Cursi
  • Marcus Kalander
  • Shuang Wu 0005
  • Xidi Xue
  • Yu Tian
  • Guangjian Tian
  • Xingyue Quan
  • Jianye Hao

Playing table tennis with a human player is a challenging robotic task due to its dynamic nature. Despite a number of researches being devoted to developing robotic table tennis systems, most of the works have demanding hardware requirements and ignore safety measures when generating the swing stoke. To address these issues, we propose a safe motion planning framework that fully pushes the robotic hardware performance limits to play table tennis. In particular, we propose a pipeline to generate manipulator joint trajectories with environmental safety constraints and scale the trajectories to satisfy joint movement limitations. We use three different agents to validate the planning algorithm with our handmade robot platform in both simulation and real-world environments.

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YNIMG Journal 2024 Journal Article

Transcriptional patterns of the cortical Morphometric Inverse Divergence in first-episode, treatment-naïve early-onset schizophrenia

  • Guanqun Yao
  • Jing Luo
  • Ting Zou
  • Jing Li
  • Shuang Hu
  • Langxiong Yang
  • Xinrong Li
  • Yu Tian

Early-onset Schizophrenia (EOS) is a profoundly progressive psychiatric disorder characterized by both positive and negative symptoms, whose pathogenesis is influenced by genes, environment and brain structure development. In this study, the MIND (Morphometric Inverse Divergence) network was employed to explore the relationship between morphological similarity and specific transcriptional expression patterns in EOS patients. This study involved a cohort of 187 participants aged between 7 and 17 years, consisting of 97 EOS patients and 90 healthy controls (HC). Multiple morphological features were used to construct the MIND network for all participants. Furthermore, we explored the associations between MIND network and brain-wide gene expression in EOS patients through partial least squares (PLS) regression, shared genetic predispositions with other psychiatric disorders, functional enrichment of PLS weighted genes, as well as transcriptional signature assessment of cell types, cortical layers, and developmental stages. The MIND showed similarity differences in the orbitofrontal cortex, pericalcarine cortex, lingual gyrus, and multiple networks in EOS patients compared to HC. Moreover, our exploration revealed a significant overlap of PLS2 weighted genes linking to EOS-related MIND differences and the dysregulated genes reported in other psychiatric diseases. Interestingly, genes correlated with MIND changes (PLS2-) exhibited a significant enrichment not only in metabolism-related pathways, but also in specific astrocytes, cortical layers (specifically layer I and III), and posterior developmental stages (late infancy to young adulthood stages). However, PLS2+ genes were primarily enriched in synapses signaling-related pathways and early developmental stages (from early-mid fetal to neonatal early infancy) but not in special cell types or layers. These findings provide a novel perspective on the intricate relationship between macroscopic morphometric structural abnormalities and microscopic transcriptional patterns during the onset and progression of EOS.

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JBHI Journal 2023 Journal Article

Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma

  • Min Shi
  • Anagha Lokhande
  • Mojtaba S. Fazli
  • Vishal Sharma
  • Yu Tian
  • Yan Luo
  • Louis R. Pasquale
  • Tobias Elze

Ophthalmic images, along with their derivatives like retinal nerve fiber layer (RNFL) thickness maps, play a crucial role in detecting and monitoring eye diseases such as glaucoma. For computer-aided diagnosis of eye diseases, the key technique is to automatically extract meaningful features from ophthalmic images that can reveal the biomarkers (e. g. , RNFL thinning patterns) associated with functional vision loss. However, representation learning from ophthalmic images that links structural retinal damage with human vision loss is non-trivial mostly due to large anatomical variations between patients. This challenge is further amplified by the presence of image artifacts, commonly resulting from image acquisition and automated segmentation issues. In this paper, we present an artifact-tolerant unsupervised learning framework called EyeLearn for learning ophthalmic image representations in glaucoma cases. EyeLearn includes an artifact correction module to learn representations that optimally predict artifact-free images. In addition, EyeLearn adopts a clustering-guided contrastive learning strategy to explicitly capture the affinities within and between images. During training, images are dynamically organized into clusters to form contrastive samples, which encourage learning similar or dissimilar representations for images in the same or different clusters, respectively. To evaluate EyeLearn, we use the learned representations for visual field prediction and glaucoma detection with a real-world dataset of glaucoma patient ophthalmic images. Extensive experiments and comparisons with state-of-the-art methods confirm the effectiveness of EyeLearn in learning optimal feature representations from ophthalmic images.

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JBHI Journal 2023 Journal Article

Integrating Medical Domain Knowledge for Early Diagnosis of Fever of Unknown Origin: An Interpretable Hierarchical Multimodal Neural Network Approach

  • Zhixiao Wang
  • Jian Liu
  • Yu Tian
  • Tianshu Zhou
  • Qianghua Liu
  • Yunqing Qiu
  • Jingsong Li

Accurate and interpretable differential diagnostic technologies are crucial for supporting clinicians in decision-making and treatment-planning for patients with fever of unknown origin (FUO). Existing solutions commonly address the diagnosis of FUO by transforming it into a multi-classification task. However, after the emergence of COVID-19 pandemic, clinicians have recognized the heightened significance of early diagnosis in patients with FUO, particularly for practical needs such as early triage. This has resulted in increased demands for identifying a wider range of etiologies, shorter observation windows, and better model interpretability. In this article, we propose an interpretable hierarchical multimodal neural network framework (iHMNNF) to facilitate early diagnosis of FUO by incorporating medical domain knowledge and leveraging multimodal clinical data. The iHMNNF comprises a top-down hierarchical reasoning framework (Td-HRF) built on the class hierarchy of FUO etiologies, five local attention-based multimodal neural networks (La-MNNs) trained for each parent node of the class hierarchy, and an interpretable module based on layer-wise relevance propagation (LRP) and attention mechanism. Experimental datasets were collected from electronic health records (EHRs) at a large-scale tertiary grade-A hospital in China, comprising 34, 051 hospital admissions of 30, 794 FUO patients from January 2011 to October 2020. Our proposed La-MNNs achieved area under the receiver operating characteristic curve (AUROC) values ranging from 0. 7809 to 0. 9035 across all five decomposed tasks, surpassing competing machine learning (ML) and single-modality deep learning (DL) methods while also providing enhanced interpretability. Furthermore, we explored the feasibility of identifying FUO etiologies using only the first N -hour time series data obtained after admission.

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AIIM Journal 2022 Journal Article

A method for the early prediction of chronic diseases based on short sequential medical data

  • Chengkai Wu
  • Tianshu Zhou
  • Yu Tian
  • Junya Wu
  • Jingsong Li
  • Zhong Liu

Noncommunicable diseases (NCDs) have become the leading cause of death worldwide. NCDs' chronicity, hiddenness, and irreversibility make patients' disease self-awareness extremely important in disease control but hard to achieve. With an accumulation of electronic health record (EHR) data, it has become possible to predict NCDs early through machine learning approaches. However, EHR data from latent NCD patients are often irregularly sampled temporally, and the data sequences are short and imbalanced, which prevents researchers from fully and effectively using such data. Here, we outline the characteristics of typical short sequential data for NCD early prediction and emphasize the importance of using such data in machine learning schemes. We then propose a novel NCD early prediction method: the short sequential medical data-based early prediction method (SSEPM). The SSEPM network contains two stacked subnetworks for multilabel enhancement. In each subnetwork, long short-term memory (LSTM) and attention layers are implemented to extract both temporal and nontemporal embedded features. During training, with prior clinical knowledge of the NCD characteristics, a random connection (RC) process is proposed for data augmentation. Comparative experiments involving ten-fold cross-validation are performed with real-world medical data to predict 5 NCDs. The result shows that the SSEPM outperforms the state-of-the-art NCD early prediction algorithms and works well in dealing with short sequential data. The results also suggest that the direct use of short sequential data could be more effective than formatting datasets with temporal exclusion limitations.

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AIIM Journal 2022 Journal Article

A novel lifelong machine learning-based method to eliminate calibration drift in clinical prediction models

  • Shengqiang Chi
  • Yu Tian
  • Feng Wang
  • Tianshu Zhou
  • Shan Jin
  • Jingsong Li

Objective Clinical prediction models (CPMs) constructed based on artificial intelligence have been proven to have positive impacts on clinical activities. However, the deterioration of CPM performance over time has rarely been studied. This paper proposes a model updating method to solve the calibration drift issue caused by data drift. Materials and methods This paper proposes a novel model updating method based on lifelong machine learning (LML). The effectiveness of the proposed method is verified in four tumor datasets, and a comprehensive comparison with other model updating methods is performed. Results Changes in data distributions cause model performances to drift. The four compared model updating methods have different effects in terms of improving the discrimination and calibration abilities of the tested models. The LML method proposed in this study improves model performance better than or equivalent to the other methods. The proposed method achieved a mean AUC of 0. 8249, 0. 8780, 0. 8261, and 0. 8489, a mean AUPRC of 0. 7782, 0. 9730, 0. 4655, and 0. 5728, a mean F1 of 0. 6866, 0. 9552, 0. 2985, and 0. 3585, and a mean estimated calibration index (ECI) of 0. 0320, 0. 0338, 0. 0101, and 0. 0115 using colorectal, lung, breast and prostate cancer datasets. Discussion The LML framework simultaneously monitors model performance and the distribution of disease risk characteristics, enabling it to effectively address the performance degradation caused by gradual and sudden data drifts and provide reasonable explanations for the causes of performance degradation. Conclusion Monitoring model performance and the underlying data distribution can promote model life cycle iteration with “development-deployment-maintenance-monitoring” as the core, which, in turn, ensures that the model can provide accurate predictions, guides the model update process and explains the causes of model performance changes.

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AAAI Conference 2022 Conference Paper

Deep One-Class Classification via Interpolated Gaussian Descriptor

  • Yuanhong Chen
  • Yu Tian
  • Guansong Pang
  • Gustavo Carneiro

One-class classification (OCC) aims to learn an effective data description to enclose all normal training samples and detect anomalies based on the deviation from the data description. Current state-of-the-art OCC models learn a compact normality description by hyper-sphere minimisation, but they often suffer from overfitting the training data, especially when the training set is small or contaminated with anomalous samples. To address this issue, we introduce the interpolated Gaussian descriptor (IGD) method, a novel OCC model that learns a one-class Gaussian anomaly classifier trained with adversarially interpolated training samples. The Gaussian anomaly classifier differentiates the training samples based on their distance to the Gaussian centre and the standard deviation of these distances, offering the model a discriminability w. r. t. the given samples during training. The adversarial interpolation is enforced to consistently learn a smooth Gaussian descriptor, even when the training data is small or contaminated with anomalous samples. This enables our model to learn the data description based on the representative normal samples rather than fringe or anomalous samples, resulting in significantly improved normality description. In extensive experiments on diverse popular benchmarks, including MNIST, Fashion MNIST, CIFAR10, MVTec AD and two medical datasets, IGD achieves better detection accuracy than current state-of-the-art models. IGD also shows better robustness in problems with small or contaminated training sets.

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ICRA Conference 2022 Conference Paper

Design of a Biomimetic Tactile Sensor for Material Classification

  • Kevin Dai
  • Xinyu Wang
  • Allison M. Rojas
  • Evan Harber
  • Yu Tian
  • Nicholas Paiva
  • Joseph Gnehm
  • Evan Schindewolf

Tactile sensing typically involves active exploration of unknown surfaces and objects, making it especially effective at processing the characteristics of materials and textures. A key property extracted by human tactile perception in material classification is surface roughness, which relies on measuring vibratory signals using the multi-layered fingertip structure. Existing robotic systems lack tactile sensors that are able to provide high dynamic sensing ranges, perceive material properties, and maintain a low hardware cost. In this work, we introduce the reference design and fabrication procedure of a miniature and low-cost tactile sensor consisting of a biomimetic cutaneous structure, including the artificial fingerprint, dermis, epidermis, and an embedded magnet-sensor structure which serves as a mechanoreceptor for converting mechanical information to digital signals. The presented sensor is capable of detecting high-resolution magnetic field data through the Hall effect and creating high-dimensional time-frequency domain features for material texture classification. Additionally, we investigate the effects of different superficial sensor fingerprint patterns for classifying materials through both simulation and physical experimentation. After extracting time series and frequency domain features, we assess a k-nearest neighbors classifier for distinguishing between different materials. The results from our experiments show that our biomimetic tactile sensors with fingerprint ridges can classify materials with more than 7. 7% higher accuracy and lower variability than ridge-less sensors. These results, along with the low cost and customizability of our sensor, demonstrate high potential for lowering the barrier to entry for a wide array of robotic applications, including modelless tactile sensing for texture classification, material inspection, and object recognition.

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IJCAI Conference 2022 Conference Paper

Hypertron: Explicit Social-Temporal Hypergraph Framework for Multi-Agent Forecasting

  • Yu Tian
  • Xingliang Huang
  • Ruigang Niu
  • Hongfeng Yu
  • Peijin Wang
  • Xian Sun

Forecasting the future trajectories of multiple agents is a core technology for human-robot interaction systems. To predict multi-agent trajectories more accurately, it is inevitable that models need to improve interpretability and reduce redundancy. However, many methods adopt implicit weight calculation or black-box networks to learn the semantic interaction of agents, which obviously lack enough interpretation. In addition, most of the existing works model the relation among all agents in a one-to-one manner, which might lead to irrational trajectory predictions due to its redundancy and noise. To address the above issues, we present Hypertron, a human-understandable and lightweight hypergraph-based multi-agent forecasting framework, to explicitly estimate the motions of multiple agents and generate reasonable trajectories. The framework explicitly interacts among multiple agents and learns their latent intentions by our coarse-to-fine hypergraph convolution interaction module. Our experiments on several challenging real-world trajectory forecasting datasets show that Hypertron outperforms a wide array of state-of-the-art methods while saving over 60% parameters and reducing 30% inference time.

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JBHI Journal 2022 Journal Article

Optimization of Dry Weight Assessment in Hemodialysis Patients via Reinforcement Learning

  • Ziyue Yang
  • Yu Tian
  • Tianshu Zhou
  • Yilin Zhu
  • Ping Zhang
  • Jianghua Chen
  • Jingsong Li

Dry weight (DW), defined as the lowest tolerated postdialysis weight following the ultrafiltration (UF) of excess fluid volume, is essential for any dialysis prescription for hemodialysis (HD) patients. However, there is no gold standard for DW assessment, and the difficulty of its accurate assessment increases given individual variations and the dynamic changes caused by the uncertainty of patients’ condition. Therefore, the current empirical evaluation process is often crude, imprecise, experience-dependent, and energy-consuming. Here, we highlight the personalized dynamic changes in DW over time rather than the more accurate DW assessments at some point in time and formulate the DW evaluation problem into a sequential decision-making process using the Markov decision process (MDP) framework. A reinforcement learning (RL) algorithm based on a dueling double deep Q-network (Duel-DDQN) is proposed to optimize the DW assessment policy, and a multifaceted inspection is applied to assess policy effectiveness and safety. We utilize ten years of data from the Kidney Disease Center, enrolling 750 HD patients and 243, 287 dialysis sessions. Good model calibration is confirmed, and off-policy evaluation demonstrates that our policy outperforms other policies, suggesting a decrease of 7. 71% in the expected 5-year mortality rate and of 13. 44% in the incidence of intradialytic symptoms compared with those of clinicians’ strategy. The RL policy adjusts DW more frequently, responds to DW changes more actively, and observes a larger feature space. It is hoped that the proposed solution will help clinicians assess and monitor DW dynamically, making the estimation process more refined, personalized, and intelligent.

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IROS Conference 2022 Conference Paper

Steady-State Manifold of Riderless Motorcycles

  • Yu Tian
  • Zhang Chen
  • Yang Deng 0001
  • Boyi Wang
  • Bin Liang 0001

Keeping balance is one of the most important tasks of a motorcycle. The steady-state manifold is proposed in this paper to explore the inherent dynamics and the balance properties of a riderless motorcycle. The dynamic and kinematic characteristics are analyzed based on the manifold and are validated by simulation. Comparing to traditional control method, the usefulness of the manifold in control is shown through the design of a novel control strategy. Furthermore, based on the analysis and the simulation, the potential applications of the manifold for control and planning are summarized.

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JBHI Journal 2021 Journal Article

Automatic Pancreas Segmentation in CT Images With Distance-Based Saliency-Aware DenseASPP Network

  • Peijun Hu
  • Xiang Li
  • Yu Tian
  • Tianyu Tang
  • Tianshu Zhou
  • Xueli Bai
  • Shiqiang Zhu
  • Tingbo Liang

Pancreas identification and segmentation is an essential task in the diagnosis and prognosis of pancreas disease. Although deep neural networks have been widely applied in abdominal organ segmentation, it is still challenging for small organs (e. g. pancreas) that present low contrast, highly flexible anatomical structure and relatively small region. In recent years, coarse-to-fine methods have improved pancreas segmentation accuracy by using coarse predictions in the fine stage, but only object location is utilized and rich image context is neglected. In this paper, we propose a novel distance-based saliency-aware model, namely DSD-ASPP-Net, to fully use coarse segmentation to highlight the pancreas feature and boost accuracy in the fine segmentation stage. Specifically, a DenseASPP (Dense Atrous Spatial Pyramid Pooling) model is trained to learn the pancreas location and probability map, which is then transformed into saliency map through geodesic distance-based saliency transformation. In the fine stage, saliency-aware modules that combine saliency map and image context are introduced into DenseASPP to develop the DSD-ASPP-Net. The architecture of DenseASPP brings multi-scale feature representation and achieves larger receptive field in a denser way, which overcome the difficulties brought by variable object sizes and locations. Our method was evaluated on both public NIH pancreas dataset and local hospital dataset, and achieved an average Dice-Sørensen Coefficient (DSC) value of 85. 49±4. 77% on the NIH dataset, outperforming former coarse-to-fine methods.

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JBHI Journal 2021 Journal Article

Deep Semisupervised Multitask Learning Model and Its Interpretability for Survival Analysis

  • Shengqiang Chi
  • Yu Tian
  • Feng Wang
  • Yu Wang
  • Ming Chen
  • Jingsong Li

Survival analysis is a commonly used method in the medical field to analyze and predict the time of events. In medicine, this approach plays a key role in determining the course of treatment, developing new drugs, and improving hospital procedures. Most of the existing work in this area has addressed the problem by making strong assumptions about the underlying stochastic process. However, these assumptions are usually violated in the real-world data. This paper proposed a semisupervised multitask learning (SSMTL) method based on deep learning for survival analysis with or without competing risks. SSMTL transforms the survival analysis problem into a multitask learning problem that includes semisupervised learning and multipoint survival probability prediction. The distribution of survival times and the relationship between covariates and outcomes were modeled directly without any assumptions. Semisupervised loss and ranking loss are used to deal with censored data and the prior knowledge of the nonincreasing trend of the survival probability. Additionally, the importance of prognostic factors is determined, and the time-dependent and nonlinear effects of these factors on survival outcomes are visualized. The prediction performance of SSMTL is better than that of previous models in settings with or without competing risks, and the effects of predictors are successfully described. This study is of great significance for the exploration and application of deep learning methods involving medical structured data and provides an effective deep-learning-based method for survival analysis with complex-structured clinical data.

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JBHI Journal 2021 Journal Article

EHR-Oriented Knowledge Graph System: Toward Efficient Utilization of Non-Used Information Buried in Routine Clinical Practice

  • Yong Shang
  • Yu Tian
  • Min Zhou
  • Tianshu Zhou
  • Kewei Lyu
  • Zhixiao Wang
  • Ran Xin
  • Tingbo Liang

Non-used clinical information has negative implications on healthcare quality. Clinicians pay priority attention to clinical information relevant to their specialties during routine clinical practices but may be insensitive or less concerned about information showing disease risks beyond their specialties, resulting in delayed and missed diagnoses or improper management. In this study, we introduced an electronic health record (EHR)-oriented knowledge graph system to efficiently utilize non-used information buried in EHRs. EHR data were transformed into a semantic patient-centralized information model under the ontology structure of a knowledge graph. The knowledge graph then creates an EHR data trajectory and performs reasoning through semantic rules to identify important clinical findings within EHR data. A graphical reasoning pathway illustrates the reasoning footage and explains the clinical significance for clinicians to better understand the neglected information. An application study was performed to evaluate unconsidered chronic kidney disease (CKD) reminding for non-nephrology clinicians to identify important neglected information. The study covered 71, 679 patients in non-nephrology departments. The system identified 2, 774 patients meeting CKD diagnosis criteria and 10, 377 patients requiring high attention. A follow-up study of 5, 439 patients showed that 82. 1% of patients who met the diagnosis criteria and 61. 4% of patients requiring high attention were confirmed to be CKD positive during follow-up research. The application demonstrated that the proposed approach is feasible and effective in clinical information utilization. Additionally, it's valuable as an explainable artificial intelligence to provide interpretable recommendations for specialist physicians to understand the importance of non-used data and make comprehensive decisions.

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AIIM Journal 2021 Journal Article

Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network

  • Jin Li
  • Yu Tian
  • Runze Li
  • Tianshu Zhou
  • Jun Li
  • Kefeng Ding
  • Jingsong Li

Background and objective Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, research on multicenter clinical collaborative networks, which can provide external medical data, has received increasing attention. With the increasing availability of machine learning techniques such as transfer learning, leveraging large-scale patient data from multiple hospitals to build data-driven predictive models with clinical application potential provides an alternative solution to address the problem of limited patient data. Methods A multicenter hybrid semi-supervised transfer learning model (MHSTL) is proposed in this study on the basis of unified common data model to ensure multicenter data standardized representation. Then the hospital-specific features, along with the co-occurrence features across domains, are aligned through a representation learning architecture that is built based on deep neural networks and the newly proposed neural decision forest model. In this process, limited patient data from the target hospital, both labeled and unlabeled, are incorporated during the feature adaptation process, thereby contributing to better model performance. Without patient-level data sharing, the proposed model learning strategy which overcomes feature misalignment and distribution divergence, enables the multi-source transfer learning process in the case of insufficient and unlabeled patient data at target hospital. Results The effectiveness of the proposed transfer learning model was evaluated on a collaborative research network of colorectal cancer patients in the US and China. The results demonstrate that the proposed model can achieve much better performance for predicting target risk with limited resources on patient data than baseline models. Better discrimination and calibration ability are also observed when sufficient labeled data are not available in the target hospital for prognosis prediction tasks. Further exploratory experiments show that the proposed approach exhibits good model generalizability regardless of the data heterogeneity. With the help of the SHapley Additive exPlanations for model interpretation, the effectiveness of incorporating hospital-specific features in the transfer learning model is shown. Conclusions In this study, the proposed method can develop prediction models from multiple source hospitals and exhibit good performance by leveraging cross-domain hospital-specific feature information, therefore enhancing the model prediction when applied to single medical institution with limited patient data.

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JBHI Journal 2021 Journal Article

Method of Tumor Pathological Micronecrosis Quantification Via Deep Learning From Label Fuzzy Proportions

  • Qiancheng Ye
  • Qi Zhang
  • Yu Tian
  • Tianshu Zhou
  • Hongbin Ge
  • Jiajun Wu
  • Na Lu
  • Xueli Bai

The presence of necrosis is associated with tumor progression and patient outcomes in many cancers, but existing analyses rarely adopt quantitative methods because the manual quantification of histopathological features is too expensive. We aim to accurately identify necrotic regions on hematoxylin and eosin (HE)–stained slides and to calculate the ratio of necrosis with minimal annotations on the images. An adaptive method named Learning from Label Fuzzy Proportions (LLFP) was introduced to histopathological image analysis. Two datasets of liver cancer HE slides were collected to verify the feasibility of the method by training on the internal set using cross validation and performing validation on the external set, along with ensemble learning to improve performance. The models from cross validation performed relatively stably in identifying necrosis, with a Concordance Index of the Slide Necrosis Score (CISNS) of 0. 9165±0. 0089 in the internal test set. The integration model improved the CISNS to 0. 9341 and achieved a CISNS of 0. 8278 on the external set. There were significant differences in survival (p = 0. 0060) between the three groups divided according to the calculated necrosis ratio. The proposed method can build an integration model good at distinguishing necrosis and capable of clinical assistance as an automatic tool to stratify patients with different risks or as a cluster tool for the quantification of histopathological features. We presented a method effective for identifying histopathological features and suggested that the extent of necrosis, especially micronecrosis, in liver cancer is related to patient outcomes.

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JBHI Journal 2021 Journal Article

Multicenter Privacy-Preserving Cox Analysis Based on Homomorphic Encryption

  • Yao Lu
  • Yu Tian
  • Tianshu Zhou
  • Shiqiang Zhu
  • Jingsong Li

The Cox proportional hazards model is one of the most widely used methods for analyzing survival data. Data from multiple data providers are required to improve the generalizability and confidence of the results of Cox analysis; however, such data sharing may result in leakage of sensitive information, leading to financial fraud, social discrimination or unauthorized data abuse. Some privacy-preserving Cox regression protocols have been proposed in past years, but they lack either security or functionality. In this paper, we propose a privacy-preserving Cox regression protocol for multiple data providers and researchers. The proposed protocol allows researchers to train models on horizontally or vertically partitioned datasets while providing privacy protection for both the sensitive data and the trained models. Our protocol utilizes threshold homomorphic encryption to guarantee security. Experimental results demonstrate that with the proposed protocol, Cox regression model training over 9 variables in a dataset of 113, 035 samples takes approximately 44 min, and the trained model is almost the same as that obtained with the original nonsecure Cox regression protocol; therefore, our protocol is a potential candidate for practical real-world applications in multicenter medical research.

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AIIM Journal 2020 Journal Article

A multicenter random forest model for effective prognosis prediction in collaborative clinical research network

  • Jin Li
  • Yu Tian
  • Yan Zhu
  • Tianshu Zhou
  • Jun Li
  • Kefeng Ding
  • Jingsong Li

Background The accuracy of a prognostic prediction model has become an essential aspect of the quality and reliability of the health-related decisions made by clinicians in modern medicine. Unfortunately, individual institutions often lack sufficient samples, which might not provide sufficient statistical power for models. One mitigation is to expand data collection from a single institution to multiple centers to collectively increase the sample size. However, sharing sensitive biomedical data for research involves complicated issues. Machine learning models such as random forests (RF), though they are commonly used and achieve good performances for prognostic prediction, usually suffer worse performance under multicenter privacy-preserving data mining scenarios compared to a centrally trained version. Methods and materials In this study, a multicenter random forest prognosis prediction model is proposed that enables federated clinical data mining from horizontally partitioned datasets. By using a novel data enhancement approach based on a differentially private generative adversarial network customized to clinical prognosis data, the proposed model is able to provide a multicenter RF model with performances on par with—or even better than—centrally trained RF but without the need to aggregate the raw data. Moreover, our model also incorporates an importance ranking step designed for feature selection without sharing patient-level information. Result The proposed model was evaluated on colorectal cancer datasets from the US and China. Two groups of datasets with different levels of heterogeneity within the collaborative research network were selected. First, we compare the performance of the distributed random forest model under different privacy parameters with different percentages of enhancement datasets and validate the effectiveness and plausibility of our approach. Then, we compare the discrimination and calibration ability of the proposed multicenter random forest with a centrally trained random forest model and other tree-based classifiers as well as some commonly used machine learning methods. The results show that the proposed model can provide better prediction performance in terms of discrimination and calibration ability than the centrally trained RF model or the other candidate models while following the privacy-preserving rules in both groups. Additionally, good discrimination and calibration ability are shown on the simplified model based on the feature importance ranking in the proposed approach. Conclusion The proposed random forest model exhibits ideal prediction capability using multicenter clinical data and overcomes the performance limitation arising from privacy guarantees. It can also provide feature importance ranking across institutions without pooling the data at a central site. This study offers a practical solution for building a prognosis prediction model in the collaborative clinical research network and solves practical issues in real-world applications of medical artificial intelligence.

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NeurIPS Conference 2019 Conference Paper

Rethinking Kernel Methods for Node Representation Learning on Graphs

  • Yu Tian
  • Long Zhao
  • Xi Peng
  • Dimitris Metaxas

Graph kernels are kernel methods measuring graph similarity and serve as a standard tool for graph classification. However, the use of kernel methods for node classification, which is a related problem to graph representation learning, is still ill-posed and the state-of-the-art methods are heavily based on heuristics. Here, we present a novel theoretical kernel-based framework for node classification that can bridge the gap between these two representation learning problems on graphs. Our approach is motivated by graph kernel methodology but extended to learn the node representations capturing the structural information in a graph. We theoretically show that our formulation is as powerful as any positive semidefinite kernels. To efficiently learn the kernel, we propose a novel mechanism for node feature aggregation and a data-driven similarity metric employed during the training phase. More importantly, our framework is flexible and complementary to other graph-based deep learning models, e. g. , Graph Convolutional Networks (GCNs). We empirically evaluate our approach on a number of standard node classification benchmarks, and demonstrate that our model sets the new state of the art.

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IJCAI Conference 2018 Conference Paper

CR-GAN: Learning Complete Representations for Multi-view Generation

  • Yu Tian
  • Xi Peng
  • Long Zhao
  • Shaoting Zhang
  • Dimitris N. Metaxas

Generating multi-view images from a single-view input is an important yet challenging problem. It has broad applications in vision, graphics, and robotics. Our study indicates that the widely-used generative adversarial network (GAN) may learn? incomplete? representations due to the single-pathway framework: an encoder-decoder network followed by a discriminator network. We propose CR-GAN to address this problem. In addition to the single reconstruction path, we introduce a generation sideway to maintain the completeness of the learned embedding space. The two learning paths collaborate and compete in a parameter-sharing manner, yielding largely improved generality to? unseen? dataset. More importantly, the two-pathway framework makes it possible to combine both labeled and unlabeled data for self-supervised learning, which further enriches the embedding space for realistic generations. We evaluate our approach on a wide range of datasets. The results prove that CR-GAN significantly outperforms state-of-the-art methods, especially when generating from? unseen? inputs in wild conditions.

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JBHI Journal 2017 Journal Article

A Shared Decision-Making System for Diabetes Medication Choice Utilizing Electronic Health Record Data

  • Yu Wang
  • Peng-Fei Li
  • Yu Tian
  • Jing-Jing Ren
  • Jing-Song Li

The use of a shared decision-making (SDM) process in antihyperglycemic medication strategy decisions is necessary due to the complexity of the conditions of diabetes patients. Knowledge of guidelines is used as decision aids in clinical situations, and during this process, no patient health conditions are considered. In this paper, we propose an SDM system framework for type-2 diabetes mellitus (T2DM) patients that not only contains knowledge abstracted from guidelines but also employs a multilabel classification model that uses class-imbalanced electronic health record (EHR) data and that aims to provide a recommended list of available antihyperglycemic medications to help physicians and patients have an SDM conversation. The use of EHR data to serve as a decision-support component in decision aids helps physicians and patients to reach a more intuitive understanding of current health conditions and allows the tailoring of the available knowledge to each patient, leading to a more effective SDM. Real-world data from 2542 T2DM inpatient EHRs were substituted by 77 features and eight output labels, i. e. , eight antihyperglycemic medications, and these data were utilized to build and validate the recommendation model. The multilabel recommendation model exhibited stable performance in every single-label classification and showed the ability to predict minority positive cases in which the average recall value of the eight classes was 0. 9898. As a whole multilabel classifier, the recommendation model demonstrated outstanding performance, with scores of 0. 0941 for Hamming Loss, 0. 7611 for Accuracy exam, 0. 9664 for Recall exam, and 0. 8269 for F exam.

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