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Lin Lin

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21 papers
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21

AAAI Conference 2026 Conference Paper

K-ProtoDiff: Key Prototypes-Guided Diffusion for Time Series Generation

  • Yuhang Duan
  • Lin Lin
  • Xiaoshuai Wu

Time series generation is essential for advancing data-driven modeling and decision-making across a wide range of domains. However, existing approaches primarily focus on global patterns, often failing to capture local key patterns such as abrupt changes or anomalies. These key patterns are crucial for interpretability and operational decision making, as they frequently represent intervention points with significant real-world impact. To bridge this gap, we propose Key Prototypes-Guided Diffusion (K-ProtoDiff) for time series generation, a new model that learns the global data distribution while preserving localized key patterns critical for temporal dynamics. In K-ProtoDiff, we first derive time series prototype representations through adaptive self-supervised learning. Then, a key prototype assignment module is used to extract prototype weights, forming key prototype-aware representations that serve as conditional guidance for generation. During sampling, to further enhance the fidelity of key patterns during the denoising process, we propose Reflection Sampling (R-Sampling), a step-wise refinement strategy that encourages the reverse trajectory to better align with key prototype constraints. Experiments on nine real-world datasets demonstrate that K-ProtoDiff significantly outperforms state-of-the-art baselines in key pattern retention, achieving an average 77.6% improvement in key pattern preservation.

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JBHI Journal 2025 Journal Article

A Deep Learning-Based Approach for the Diagnostic of Brucellar Spondylitis in Magnetic Resonance Images

  • Dan Shao
  • Jinquan Wei
  • Binyang Wang
  • Zhijun Wang
  • Pengying Niu
  • Lvlin Yang
  • Guangzhao Zhang
  • Pu Chen

Brucellar spondylitis (BS), a prevalent zoonotic disease caused by Brucella, poses a significant global health threat. Accurate and timely diagnosis of BS is crucial for effective treatment; however, no specialized deep learning model has been developed for detecting BS in MR images. In this study, we proposed Brucella Spondylitis MRI Diagnosis Network (BSMRINet), a fully automated diagnostic framework designed for the detection of BS from T2-weighted (T2W) MR images. The model was developed and validated using 582 cohorts collected from four hospitals between January 2018 and August 2023. The BSMRINet architecture comprised two key modules. The vertebral body lesion detection module was designed to detect BS in intact vertebral bodies by integrating a corner detection algorithm with a ResNet-based deep learning model. This module provided accurate identification and localization of potential lesions of Brucella and calculated intervertebral disc height (DH) values. The spine lesion detection module was specifically designed to detect BS in damaged vertebral bodies by utilizing a DenseNet architecture with modified squeeze-and-excitation (scSE) networks. This module further evaluated paravertebral injuries, including abscess formation, soft tissue swelling, and joint involvement. BSMRINet demonstrated strong robustness and generalization across both internal and external validation phases. Additionally, it outperformed two radiologists with 10 to 15 years of experience in diagnosing spinal MR images. The results suggested that BSMRINet can assist in the diagnostic process of BS and enhance the diagnostic capabilities of radiologists.

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JBHI Journal 2025 Journal Article

Feature Separation in Diffuse Lung Disease Image Classification by Using Evolutionary Algorithm-Based NAS

  • Qing Zhang
  • Dan Shao
  • Lin Lin
  • Guoliang Gong
  • Rui Xu
  • Shoji Kido
  • HongWei Cui

In the field of diagnosing lung diseases, the application of neural networks (NNs) in image classification exhibits significant potential. However, NNs are considered “black boxes, ” making it difficult to discern their decision-making processes, thereby leading to skepticism and concern regarding NNs. This compromises model reliability and hampers intelligent medicine's development. To tackle this issue, we introduce the Evolutionary Neural Architecture Search (EvoNAS). In image classification tasks, EvoNAS initially utilizes an Evolutionary Algorithm to explore various Convolutional Neural Networks, ultimately yielding an optimized network that excels at separating between redundant texture features and the most discriminative ones. Retaining the most discriminative features improves classification accuracy, particularly in distinguishing similar features. This approach illuminates the intrinsic mechanics of classification, thereby enhancing the accuracy of the results. Subsequently, we incorporate a Differential Evolution algorithm based on distribution estimation, significantly enhancing search efficiency. Employing visualization techniques, we demonstrate the effectiveness of EvoNAS, endowing the model with interpretability. Finally, we conduct experiments on the diffuse lung disease texture dataset using EvoNAS. Compared to the original network, the classification accuracy increases by 0. 56%. Moreover, our EvoNAS approach demonstrates significant advantages over existing methods in the same dataset.

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JBHI Journal 2025 Journal Article

Global Habitat Analysis with Multi-graph Fusion Framework of Postoperative MRI for Predicting Radiotherapy Treatment Response in Glioma Patients

  • Yixin Wang
  • Lin Lin
  • Zongtao Hu
  • Hongzhi Wang
  • Qiupu Chen

Traditional methods for predicting treatment response often rely on readily available clinical factors. However, these methods often lack the granularity to capture the complex interplay between tumor heterogeneity and treatment efficacy. A Multi-graph Fusion (MGF) model that uses habitat subregion-derived radiomic features may help predicting the response to radiotherapy in glioma patients. Firstly, three structural and three physiological habitat regions were delineated using multi-parametric magnetic resonance imaging sequences. Then radiomic features derived from these habitat subregions were used to construct MGF model, which were trained on different combinations of habitat subregions. Each view corresponded to a graph constructed from a specific tumor habitat subregion. Lastly, proposed multi-view fusion module was employed to interpret critical views and interactions for predicting treatment response, while GNNExplainer was used to elucidate the contributions of each view. The MGF model incorporating all habitats achieved the highest area under the curve values of 0. 848 (95% CI: 0. 832–0. 863) for the training cohort and 0. 792 (95% CI: 0. 767–0. 818) for the validation cohort in predicting treatment response. The attention values indicated that physiological habitat 3 held the highest significance. The GNNExplainer revealed key nodes and radiomic features in each view. The MGF model utilizing all habitats-derived radiomics demonstrated the best performance in predicting treatment response. The combination of multi-view fusion module and GNNExplainer enables the framework to capture complex contextual information across six habitat subregions and provides interpretability regarding the factors influencing treatment response predictions.

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AAAI Conference 2025 System Paper

MathMistake Checker: A Comprehensive Demonstration for Step-by-Step Math Problem Mistake Finding by Prompt-Guided LLMs

  • Tianyang Zhang
  • Zhuoxuan Jiang
  • Haotian Zhang
  • Lin Lin
  • Shaohua Zhang

We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision and the chain-of-thought capabilities of the latest large language models (LLMs). Our system supports open-ended grading without reference answers and promotes personalized learning by providing targeted feedback. We demonstrate its effectiveness across various types of math problems, such as calculation and word problems.

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JBHI Journal 2025 Journal Article

SecProGNN: Predicting Bronchoalveolar Lavage Fluid Secreted Protein Using Graph Neural Network

  • Dan Shao
  • Guangzhao Zhang
  • Lin Lin
  • Yucong Xiong
  • Kai He
  • Liyan Sun

Bronchoalveolar lavage fluid (BALF) is a liquid obtained from the alveoli and bronchi, often used to study pulmonary diseases. So far, proteomic analyses have identified over three thousand proteins in BALF. However, the comprehensive characterization of these proteins remains challenging due to their complexity and technological limitations. This paper presented a novel deep learning framework called SecProGNN, designed to predict secretory proteins in BALF. Firstly, SecProGNN represented proteins as graph-structured data, with amino acids connected based on their interactions. Then, these graphs were processed through graph neural networks (GNNs) model to extract graph features. Finally, the extracted feature vectors were fed into a multi-layer perceptron (MLP) module to predict BALF secreted proteins. Additionally, by utilizing SecProGNN, we investigated potential biomarkers for lung adenocarcinoma and identified 16 promising candidates that may be secreted into BALF.

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JMLR Journal 2024 Journal Article

A Semi-parametric Estimation of Personalized Dose-response Function Using Instrumental Variables

  • Wei Luo
  • Yeying Zhu
  • Xuekui Zhang
  • Lin Lin

In the application of instrumental variable analysis that conducts causal inference in the presence of unmeasured confounding, invalid instrumental variables and weak instrumental variables often exist which complicate the analysis. In this paper, we propose a model-free dimension reduction procedure to select the invalid instrumental variables and refine them into lower-dimensional linear combinations. The procedure also combines the weak instrumental variables into a few stronger instrumental variables that best condense their information. We then introduce the personalized dose-response function that incorporates the subject's personal characteristics into the conventional dose-response function, and use the reduced data from dimension reduction to propose a novel and easily implementable nonparametric estimator of this function. The proposed approach is suitable for both discrete and continuous treatment variables, and is robust to the dimensionality of data. Its effectiveness is illustrated by the simulation studies and the data analysis of ADNI-DoD study, where the causal relationship between depression and dementia is investigated. [abs] [ pdf ][ bib ] &copy JMLR 2024. ( edit, beta )

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JBHI Journal 2021 Journal Article

Joint Extraction of Retinal Vessels and Centerlines Based on Deep Semantics and Multi-Scaled Cross-Task Aggregation

  • Rui Xu
  • Tiantian Liu
  • Xinchen Ye
  • Fei Liu
  • Lin Lin
  • Liang Li
  • Satoshi Tanaka
  • Yen-Wei Chen

Retinal vessel segmentation and centerline extraction are crucial steps in building a computer-aided diagnosis system on retinal images. Previous works treat them as two isolated tasks, while ignoring their tight association. In this paper, we propose a deep semantics and multi-scaled cross-task aggregation network that takes advantage of the association to jointly improve their performances. Our network is featured by two sub-networks. The forepart is a deep semantics aggregation sub-network that aggregates strong semantic information to produce more powerful features for both tasks, and the tail is a multi-scaled cross-task aggregation sub-network that explores complementary information to refine the results. We evaluate the proposed method on three public databases, which are DRIVE, STARE and CHASE_DB1. Experimental results show that our method can not only simultaneously extract retinal vessels and their centerlines but also achieve the state-of-the-art performances on both tasks.

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NeurIPS Conference 2018 Conference Paper

Explaining Deep Learning Models -- A Bayesian Non-parametric Approach

  • Wenbo Guo
  • Sui Huang
  • Yunzhe Tao
  • Xinyu Xing
  • Lin Lin

Understanding and interpreting how machine learning (ML) models make decisions have been a big challenge. While recent research has proposed various technical approaches to provide some clues as to how an ML model makes individual predictions, they cannot provide users with an ability to inspect a model as a complete entity. In this work, we propose a novel technical approach that augments a Bayesian non-parametric regression mixture model with multiple elastic nets. Using the enhanced mixture model, we can extract generalizable insights for a target model through a global approximation. To demonstrate the utility of our approach, we evaluate it on different ML models in the context of image recognition. The empirical results indicate that our proposed approach not only outperforms the state-of-the-art techniques in explaining individual decisions but also provides users with an ability to discover the vulnerabilities of the target ML models.

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JMLR Journal 2017 Journal Article

Clustering with Hidden Markov Model on Variable Blocks

  • Lin Lin
  • Jia Li

Large-scale data containing multiple important rare clusters, even at moderately high dimensions, pose challenges for existing clustering methods. To address this issue, we propose a new mixture model called Hidden Markov Model on Variable Blocks (HMM-VB) and a new mode search algorithm called Modal Baum-Welch (MBW) for mode-association clustering. HMM-VB leverages prior information about chain-like dependence among groups of variables to achieve the effect of dimension reduction. In case such a dependence structure is unknown or assumed merely for the sake of parsimonious modeling, we develop a recursive search algorithm based on BIC to optimize the formation of ordered variable blocks. The MBW algorithm ensures the feasibility of clustering via mode association, achieving linear complexity in terms of the number of variable blocks despite the exponentially growing number of possible state sequences in HMM-VB. In addition, we provide theoretical investigations about the identifiability of HMM-VB as well as the consistency of our approach to search for the block partition of variables in a special case. Experiments on simulated and real data show that our proposed method outperforms other widely used methods. [abs] [ pdf ][ bib ] &copy JMLR 2017. ( edit, beta )

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TIST Journal 2013 Journal Article

Web media semantic concept retrieval via tag removal and model fusion

  • Chao Chen
  • Qiusha Zhu
  • Lin Lin
  • Mei-Ling Shyu

Multimedia data on social websites contain rich semantics and are often accompanied with user-defined tags. To enhance Web media semantic concept retrieval, the fusion of tag-based and content-based models can be used, though it is very challenging. In this article, a novel semantic concept retrieval framework that incorporates tag removal and model fusion is proposed to tackle such a challenge. Tags with useful information can facilitate media search, but they are often imprecise, which makes it important to apply noisy tag removal (by deleting uncorrelated tags) to improve the performance of semantic concept retrieval. Therefore, a multiple correspondence analysis (MCA)-based tag removal algorithm is proposed, which utilizes MCA's ability to capture the relationships among nominal features and identify representative and discriminative tags holding strong correlations with the target semantic concepts. To further improve the retrieval performance, a novel model fusion method is also proposed to combine ranking scores from both tag-based and content-based models, where the adjustment of ranking scores, the reliability of models, and the correlations between the intervals divided on the ranking scores and the semantic concepts are all considered. Comparative results with extensive experiments on the NUS-WIDE-LITE as well as the NUS-WIDE-270K benchmark datasets with 81 semantic concepts show that the proposed framework outperforms baseline results and the other comparison methods with each component being evaluated separately.

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