Fang Chen

JBHI Journal 2026 Journal Article

Feasibility Study of a Diffusion-Based Model for Cross-Modal Generation of Knee MRI From X-Ray: Integrating External Radiographic Feature Information

• Zhe Wang
• Yung Hsin Chen
• Aladine Chetouani
• Fabian Bauer
• Yuhua Ru
• Fang Chen
• Liping Zhang
• Rachid Jennane

Knee osteoarthritis (KOA) is a prevalent musculoskeletal disorder, often diagnosed using X-rays due to its cost-effectiveness. While Magnetic Resonance Imaging (MRI) provides superior soft tissue visualization and serves as a valuable supplementary diagnostic tool, its high cost and limited accessibility significantly restrict its widespread use. To explore the feasibility of bridging this imaging gap, we conducted a feasibility study leveraging a diffusion-based model that uses an X-ray image as conditional input, alongside target depth and additional patient-specific feature information, to generate corresponding MRI sequences. Our findings demonstrate that the MRI volumes generated by our approach are not only visually closer to real MRI scans compared with other methods but also achieve the highest quantitative performance in terms of Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM). Furthermore, by increasing the number of inference steps to interpolate between slice depths, we enhance the continuity of the generated volume, achieving higher adjacent slice correlation coefficients. Through ablation studies, we further validate that integrating supplemental patient-specific information, beyond what X-rays alone can provide, enhances the accuracy and clinical relevance of the generated MRI, which underscores the potential of leveraging external patient-specific information to improve the performance of the MRI generation.

EAAI Journal 2026 Journal Article

Learning unified market interdependencies via networked attention for stock price forecasting

• Kaveesha Hewage
• Boyu Li
• Ting Guo
• Alexis Stenfors
• Peter Mere
• Fang Chen

Stock price forecasting is challenging due to market volatility and complex, dynamic inter-stock relationships. Existing graph-based approaches often rely primarily on static relational structures or simple time-aligned correlations, which capture only fixed or short-term relationships and fail to model transient cross-temporal dependencies and heterogeneous information sources. We propose a novel artificial intelligence framework for stock price forecasting that integrates heterogeneous data sources, including price co-movements, corporate linkages derived from Wikipedia, and industry affiliations, into a unified dynamic relational graph that combines both structural and behavioral dependencies. The proposed model, named Learning Unified Market Interdependencies (LUMI), adaptively models evolving inter-stock connections and uncovers latent dependencies beyond sectoral or time-aligned patterns. A dual-path temporal attention mechanism disentangles long-term trends from short-term fluctuations, capturing both periodic behaviors and abrupt market shifts. Extensive experiments on four market datasets demonstrate that the proposed deep learning framework outperforms strong baselines in predictive accuracy while providing interpretable insights into market interdependencies. These findings highlight the potential of artificial intelligence for modeling complex financial systems and improving algorithmic stock price forecasting.

EAAI Journal 2026 Journal Article

Multiscale Feature Fusion Network with Morphological Perception Function for Liver Vessel Segmentation

• Xiaodong Ni
• Fang Chen
• Danpeng Ding
• Peng Shi
• Jing Zhong

Automated and accurate segmentation of liver vessels is crucial for effective clinical diagnosis and treatment planning. In current clinical practice, liver vessels are typically delineated manually on computed tomography (CT) images by physicians using specialized software, a process that is both time-consuming and labor-intensive. In addition, the inherent structural complexity and sparse distribution of liver vessels pose significant challenges to improve the efficacy of liver vessel segmentation. Most existing methods primarily focus on minimizing information loss during the downsampling process, often overlooking the utilization of information across feature channels. This oversight leads to the loss of high-level semantic details, which are crucial for accurate segmentation tasks. To address these challenges, we proposed a Residual-inspired Multiscale Feature Fusion network, called RMFF-Net, which integrates a Multiscale Feature Fusion (MSFF) module and an Attention-Guided Morphological Perception (AGMP) module. The MSFF module facilitates feature extraction across feature channels to capture intricate structural details of liver vessels. Simultaneously, the AGMP module enhances the decoding process by incorporating attention mechanisms to preserve high-level semantic information. Extensive experiments were conducted on two publicly available datasets. The results demonstrated that the proposed RMFF-Net achieves state-of-the-art performance in the segmentation of liver vessels, highlighting its significant potential for medical-assisted diagnosis and clinical applications.

AAAI Conference 2026 Conference Paper

RcAE: Recursive Reconstruction Framework for Unsupervised Industrial Anomaly Detection

• Rongcheng Wu
• Hao Zhu
• Shiying Zhang
• Mingzhe Wang
• Zhidong Li
• Hui Li
• Jianlong Zhou
• Jiangtao Cui

Unsupervised industrial anomaly detection requires accurately identifying defects without labeled data. Traditional autoencoder-based methods often struggle with incomplete anomaly suppression and loss of fine details, as their single-pass decoding fails to effectively handle anomalies with varying severity and scale. We propose a recursive architecture for autoencoder (RcAE), which performs reconstruction iteratively to progressively suppress anomalies while refining normal structures. Unlike traditional single-pass models, this recursive design naturally produces a sequence of reconstructions, progressively exposing suppressed abnormal patterns. To leverage this reconstruction dynamics, we introduce a Cross Recursion Detection (CRD) module that tracks inconsistencies across recursion steps, enhancing detection of both subtle and large-scale anomalies. Additionally, we incorporate a Detail Preservation Network (DPN) to recover high-frequency textures typically lost during reconstruction. Extensive experiments demonstrate that our method significantly outperforms existing non-diffusion methods, and achieves performance on par with recent diffusion models with only 10% of their parameters and offering substantially faster inference. These results highlight the practicality and efficiency of our approach for real-world applications.

AAAI Conference 2025 Conference Paper

LLM-RG4: Flexible and Factual Radiology Report Generation Across Diverse Input Contexts

• Zhuhao Wang
• Yihua Sun
• Zihan Li
• Xuan Yang
• Fang Chen
• Hongen Liao

Drafting radiology reports is a complex task requiring flexibility, where radiologists tail content to available information and particular clinical demands. However, most current radiology report generation (RRG) models are constrained to a fixed task paradigm, such as predicting the full ''finding'' section from a single image, inherently involving a mismatch between inputs and outputs. The trained models lack the flexibility for diverse inputs and could generate harmful, input-agnostic hallucinations. To bridge the gap between current RRG models and the clinical demands in practice, we first develop a data generation pipeline to create a new MIMIC-RG4 dataset, which considers four common radiology report drafting scenarios and has perfectly corresponded input and output. Secondly, we propose a novel large language model (LLM) based RRG framework, namely LLM-RG4, which utilizes LLM's flexible instruction-following capabilities and extensive general knowledge. We further develop an adaptive token fusion module that offers flexibility to handle diverse scenarios with different input combinations, while minimizing the additional computational burden associated with increased input volumes. Besides, we propose a token-level loss weighting strategy to direct the model's attention towards positive and uncertain descriptions. Experimental results demonstrate that LLM-RG4 achieves state-of-the-art performance in both clinical efficiency and natural language generation on the MIMIC-RG4 and MIMIC-CXR datasets. We quantitatively demonstrate that our model has minimal input-agnostic hallucinations, whereas current open-source models commonly suffer from this problem.

AAAI Conference 2025 Conference Paper

Navigating Towards Fairness with Data Selection

• Yixuan Zhang
• Zhidong Li
• Yang Wang
• Fang Chen
• Xuhui Fan
• Feng Zhou

Machine learning algorithms often struggle to eliminate inherent data biases, particularly those arising from unreliable labels, which poses a significant challenge in ensuring fairness. Existing fairness techniques that address label bias typically involve modifying models and intervening in the training process, but these lack flexibility for large-scale datasets. To address this limitation, we introduce a data selection method designed to efficiently and flexibly mitigate label bias, tailored to more practical needs. Our approach utilizes a zero-shot predictor as a proxy model that simulates training on a clean holdout set. This strategy, supported by peer predictions, ensures the fairness of the proxy model and eliminates the need for an additional holdout set, which is a common requirement in previous methods. Without altering the classifier's architecture, our modality-agnostic method effectively selects appropriate training data and has proven efficient and effective in handling label bias and improving fairness across diverse datasets in experimental evaluations.

TMLR Journal 2025 Journal Article

ReDistill: Residual Encoded Distillation for Peak Memory Reduction of CNNs

• Fang Chen
• Gourav Datta
• Mujahid Al Rafi
• Hyeran Jeon
• Meng Tang

The expansion of neural network sizes and the enhanced resolution of modern image sensors result in heightened memory and power demands to process modern computer vision models. In order to deploy these models in extremely resource-constrained edge devices, it is crucial to reduce their peak memory, which is the maximum memory consumed during the execution of a model. A naive approach to reducing peak memory is aggressive down-sampling of feature maps via pooling with large stride, which often results in unacceptable degradation in network performance. To mitigate this problem, we propose residual encoded distillation (ReDistill) for peak memory reduction in a teacher-student framework, in which a student network with less memory is derived from the teacher network using aggressive pooling. We apply our distillation method to multiple problems in computer vision, including image classification and diffusion-based image generation. For image classification, our method yields 4x-5x theoretical peak memory reduction with less degradation in accuracy for most CNN-based architectures. For diffusion-based image generation, our proposed distillation method yields a denoising network with 4x lower theoretical peak memory while maintaining decent diversity and fidelity for image generation. Experiments demonstrate our method's superior performance compared to other feature-based and response-based distillation methods when applied to the same student network. The code is available at https://github.com/mengtang-lab/ReDistill.

NeurIPS Conference 2025 Conference Paper

Revealing Multimodal Causality with Large Language Models

• Jin Li
• Shoujin Wang
• Qi Zhang
• Feng Liu
• Tongliang Liu
• Longbing Cao
• Shui Yu
• Fang Chen

Uncovering cause-and-effect mechanisms from data is fundamental to scientific progress. While large language models (LLMs) show promise for enhancing causal discovery (CD) from unstructured data, their application to the increasingly prevalent multimodal setting remains a critical challenge. Even with the advent of multimodal LLMs (MLLMs), their efficacy in multimodal CD is hindered by two primary limitations: (1) difficulty in exploring intra- and inter-modal interactions for comprehensive causal variable identification; and (2) insufficiency to handle structural ambiguities with purely observational data. To address these challenges, we propose MLLM-CD, a novel framework for multimodal causal discovery from unstructured data. It consists of three key components: (1) a novel contrastive factor discovery module to identify genuine multimodal factors based on the interactions explored from contrastive sample pairs; (2) a statistical causal structure discovery module to infer causal relationships among discovered factors; and (3) an iterative multimodal counterfactual reasoning module to refine the discovery outcomes iteratively by incorporating the world knowledge and reasoning capabilities of MLLMs. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed MLLM-CD in revealing genuine factors and causal relationships among them from multimodal unstructured data. The implementation code and data are available at https: //github. com/JinLi-i/MLLM-CD.

IS Journal 2024 Journal Article

Explaining Imitation Learning Through Frames

• Boyuan Zheng
• Jianlong Zhou
• Chunjie Liu
• Yiqiao Li
• Fang Chen

As one of the prevalent methods to achieve automation systems, imitation learning (IL) presents a promising performance in a wide range of domains. However, despite the considerable improvement in policy performance, the corresponding research on the explainability of IL models is still limited. Inspired by the recent approaches in explainable AI, we proposed a model-agnostic explaining framework for IL models called Remove and Retrain via Randomized Input Sampling for Explanation (R2RISE). R2RISE aims to explain the importance of frames with respect to the overall policy performance. It iteratively retrains the black-box IL model from the randomized masked demonstrations and uses the conventional evaluation outcome environment returns as the coefficient to build an importance map. We also conducted experiments to investigate three major questions concerning frames’ importance equality, the effectiveness of the importance map, and connections in importance maps from different IL models. The result shows that R2RISE distinguishes important frames from the demonstrations effectively.

ICRA Conference 2024 Conference Paper

RBI-RRT*: Efficient Sampling-based Path Planning for High-dimensional State Space

• Fang Chen
• Yu Zheng
• Zheng Wang
• Wanchao Chi
• Sicong Liu

Sampling-based planning algorithms such as RRT have been proved to be efficient in solving path planning problems for robotic systems. Various improvements to the RRT algorithm have been presented to improve the performance of the extension and convergence of the random trees, such as Informed RRT*. However, with the growth of spatial dimensions, the time consumption of randomly sampling the entire state space and incrementally rewiring the random trees raises drastically before a feasible solution is found. In this paper, to enhance the convergence performance of optimal solutions, we present Reconstructed Bi-directional Informed RRT* (RBI-RRT*) path planning algorithm. The algorithm acts as RRT-Connect to rapidly find a feasible solution, which helps compress the sampling space as Informed RRT* does. After the random trees are transformed into RRT* structure by the reconstruction process in RBI-RRT*, the algorithm continues to find the near-optimal path. A series of simulations and real-world robot experiments were conducted to evaluate the algorithm against existing planning algorithms. Compared to Informed RRT* Connect, RBI-RRT* reduced the computation time of achieving a specific cost by 22. 1% on average in simulations and 11. 2% in the real-world robotic arm experiments. The results show that RBI-RRT* is more efficient in high-dimensional planning problems.

JBHI Journal 2023 Journal Article

A Segmentation Framework With Unsupervised Learning-Based Label Mapper for the Ventricular Target of Intracranial Germ Cell Tumor

• Xianyu Wang
• Shuai Liu
• Ne Yang
• Fang Chen
• Longfei Ma
• Guochen Ning
• Hui Zhang
• Xiaoguang Qiu

Intracranial germ cell tumors are rare tumors that mainly affect children and adolescents. Radiotherapy is the cornerstone of interdisciplinary treatment methods. Radiation of the whole ventricle system and the local tumor can reduce the complications in the late stage of radiotherapy while ensuring the curative effect. However, manually delineating the ventricular system is labor-intensive and time-consuming for physicians. The diverse ventricle shape and the hydrocephalus-induced ventricle dilation increase the difficulty of automatic segmentation algorithms. Therefore, this study proposed a fully automatic segmentation framework. Firstly, we designed a novel unsupervised learning-based label mapper, which is used to handle the ventricle shape variations and obtain the preliminary segmentation result. Then, to boost the segmentation performance of the framework, we improved the region growth algorithm and combined the fully connected conditional random field to optimize the preliminary results from both regional and voxel scales. In the case of only one set of annotated data is required, the average time cost is 153. 01 s, and the average target segmentation accuracy can reach 84. 69%. Furthermore, we verified the algorithm in practical clinical applications. The results demonstrate that our proposed method is beneficial for physicians to delineate radiotherapy targets, which is feasible and clinically practical, and may fill the gap of automatic delineation methods for the ventricular target of intracranial germ celltumors.

IS Journal 2023 Journal Article

Artificial Intelligence Ethics and Trust: From Principles to Practice

• Fang Chen
• Jianlong Zhou
• Andreas Holzinger
• Kenneth R. Fleischmann
• Simone Stumpf

Despite the proliferation of ethical frameworks of artificial intelligence (AI) from different organizations such as government agencies, large corporations, and academic institutions, it is still a challenge to implement and operationalize ethical and legal frameworks for AI in practice due to its complexities. The implementation and operationalization involve different aspects in original theoretical and practical research on designing, developing, presenting, testing, and evaluating approaches, which are supported by advanced AI techniques and interdisciplinary research, in particular, social science, law, and cognitive science. This editorial provides an overview of the field of operationalization of AI ethics and trust, and highlights a few key topics covered in this special issue, i. e. , the current landscape of AI ethics implementation, trust and trustworthiness in AI, ethical framework for trust calibration, approaches to build morality in AI, implementation of AI ethics with a pattern-oriented engineering approach, and inclusive user studies.

JBHI Journal 2023 Journal Article

Dynamic Perfusion Representation and Aggregation Network for Nodule Segmentation Using Contrast-Enhanced US

• Peng Wan
• Haiyan Xue
• Chunrui Liu
• Fang Chen
• Wentao Kong
• Daoqiang Zhang

Dynamic contrast-enhanced ultrasound (CEUS) imaging has been widely applied in lesion detection and characterization, due to its offered real-time observation of microvascular perfusion. Accurate lesion segmentation is of great importance to the quantitative and qualitative perfusion analysis. In this paper, we propose a novel dynamic perfusion representation and aggregation network (DpRAN) for the automatic segmentation of lesions using dynamic CEUS imaging. The core challenge of this work lies in enhancement dynamics modeling of various perfusion areas. Specifically, we divide enhancement features into the two scales: short-range enhancement patterns and long-range evolution tendency. To effectively represent real-time enhancement characteristics and aggregate them in a global view, we introduce the perfusion excitation (PE) gate and cross-attention temporal aggregation (CTA) module, respectively. Different from the common temporal fusion methods, we also introduce an uncertainty estimation strategy to assist the model to locate the critical enhancement point first, in which a relatively distinguished enhancement pattern is displayed. The segmentation performance of our DpRAN method is validated on our collected CEUS datasets of thyroid nodules. We obtain the mean dice coefficient (DSC) and intersection of union (IoU) of 0. 794 and 0. 676, respectively. Superior performance demonstrates its efficacy to capture distinguished enhancement characteristics for lesion recognition.

NeurIPS Conference 2022 Conference Paper

Domain Generalization by Learning and Removing Domain-specific Features

• Yu Ding
• Lei Wang
• Bin Liang
• Shuming Liang
• Yang Wang
• Fang Chen

Deep Neural Networks (DNNs) suffer from domain shift when the test dataset follows a distribution different from the training dataset. Domain generalization aims to tackle this issue by learning a model that can generalize to unseen domains. In this paper, we propose a new approach that aims to explicitly remove domain-specific features for domain generalization. Following this approach, we propose a novel framework called Learning and Removing Domain-specific features for Generalization (LRDG) that learns a domain-invariant model by tactically removing domain-specific features from the input images. Specifically, we design a classifier to effectively learn the domain-specific features for each source domain, respectively. We then develop an encoder-decoder network to map each input image into a new image space where the learned domain-specific features are removed. With the images output by the encoder-decoder network, another classifier is designed to learn the domain-invariant features to conduct image classification. Extensive experiments demonstrate that our framework achieves superior performance compared with state-of-the-art methods.

ECAI Conference 2020 Conference Paper

A Multi-Task Learning Neural Network for Emotion-Cause Pair Extraction

• Sixing Wu
• Fang Chen
• Fangzhao Wu
• Yongfeng Huang 0001
• Xing Li 0001

Emotion-cause pair extraction, which aims at extracting both the emotion and its corresponding cause in text, is a significant and challenging task in emotion analysis. Previous work formulated the task in a two-step framework, i. e. , emotion and cause extraction, and emotion-cause relation classification. However, different tasks may correlate with each other and the two-step framework does not fully exploit the interactions between tasks. In this paper, we propose a multi-task neural network to perform emotion-cause pair extraction in a unified model. The task of relation classification is learned together with emotion and cause extraction. To this end, we develop a method to obtain training samples for relation classification without the dependence on the result of emotion and cause extraction. To fully exploit the interactions between different tasks, our model shares useful features across tasks. Moreover, we propose a method to incorporate position-aware emotion information in cause extraction to further improve the performance. Experimental results show that our model outperforms the state-of-the-art model on emotion-cause pair extraction.

JMLR Journal 2020 Journal Article

Efficient Inference for Nonparametric Hawkes Processes Using Auxiliary Latent Variables

• Feng Zhou
• Zhidong Li
• Xuhui Fan
• Yang Wang
• Arcot Sowmya
• Fang Chen

The expressive ability of classic Hawkes processes is limited due to the parametric assumption on the baseline intensity and triggering kernel. Therefore, it is desirable to perform inference in a data-driven, nonparametric approach. Many recent works have proposed nonparametric Hawkes process models based on Gaussian processes (GP). However, the likelihood is non-conjugate to the prior resulting in a complicated and time-consuming inference procedure. To address the problem, we present the sigmoid Gaussian Hawkes process model in this paper: the baseline intensity and triggering kernel are both modeled as the sigmoid transformation of random trajectories drawn from a GP. By introducing auxiliary latent random variables (branching structure, P\'{o}lya-Gamma random variables and latent marked Poisson processes), the likelihood is converted to two decoupled components with a Gaussian form which allows for an efficient conjugate analytical inference. Using the augmented likelihood, we derive an efficient Gibbs sampling algorithm to sample from the posterior; an efficient expectation-maximization (EM) algorithm to obtain the maximum a posteriori (MAP) estimate and furthermore an efficient mean-field variational inference algorithm to approximate the posterior. To further accelerate the inference, a sparse GP approximation is introduced to reduce complexity. We demonstrate the performance of our three algorithms on both simulated and real data. The experiments show that our proposed inference algorithms can recover well the underlying prompting characteristics efficiently. [abs] [ pdf ][ bib ] &copy JMLR 2020. ( edit, beta )

AAAI Conference 2020 Conference Paper

Graph-Based Decoding Model for Functional Alignment of Unaligned fMRI Data

• Weida Li
• Mingxia Liu
• Fang Chen
• Daoqiang Zhang

Aggregating multi-subject functional magnetic resonance imaging (fMRI) data is indispensable for generating valid and general inferences from patterns distributed across human brains. The disparities in anatomical structures and functional topographies of human brains warrant aligning fMRI data across subjects. However, the existing functional alignment methods cannot handle well various kinds of fMRI datasets today, especially when they are not temporally-aligned, i. e. , some of the subjects probably lack the responses to some stimuli, or different subjects might follow different sequences of stimuli. In this paper, a cross-subject graph that depicts the (dis)similarities between samples across subjects is used as a priori for developing a more flexible framework that suits an assortment of fMRI datasets. However, the high dimension of fMRI data and the use of multiple subjects makes the crude framework time-consuming or unpractical. To address this issue, we further regularize the framework, so that a novel feasible kernel-based optimization, which permits nonlinear feature extraction, could be theoretically developed. Specifically, a low-dimension assumption is imposed on each new feature space to avoid overfitting caused by the highspatial-low-temporal resolution of fMRI data. Experimental results on five datasets suggest that the proposed method is not only superior to several state-of-the-art methods on temporally-aligned fMRI data, but also suitable for dealing with temporally-unaligned fMRI data.

JBHI Journal 2020 Journal Article

Improved 3D Catheter Shape Estimation Using Ultrasound Imaging for Endovascular Navigation: A Further Study

• Fang Chen
• Jia Liu
• Xinran Zhang
• Daoqiang Zhang
• Hongen Liao

Objective: Two-dimensional fluoroscopy is the standard guidance imaging method for closed endovascular intervention. However, two-dimensional fluoroscopy lacks depth perception for the intervention catheter and causes radiation exposure for both surgeons and patients. In this paper, we extend our previous study and develop the improved three-dimensional (3D) catheter shape estimation using ultrasound imaging. In addition, we perform further quantitative evaluations of endovascular navigation. Method: First, the catheter tracking accuracy in ultrasound images is improved by adjusting the state vector and adding direction information. Then, the 3D catheter points from the catheter tracking are further optimized based on the 3D catheter shape optimization with a high-quality sample set. Finally, the estimated 3D catheter shapes from ultrasound images are overlaid with preoperative 3D tissue structures for the intuitive endovascular navigation. Results: the tracking accuracy of the catheter increased by 24. 39%, and the accuracy of the catheter shape optimization step also increased by approximately 17. 34% compared with our previous study. Furthermore, the overall error of catheter shape estimation was further validated in the catheter intervention experiment of in vitro cardiovascular tissue and in a vivo swine, and the errors were 2. 13 mm and 3. 37 mm, respectively. Conclusion: Experimental results demonstrate that the improved catheter shape estimation using ultrasound imaging is accurate and appropriate for endovascular navigation. Significance: Improved navigation reduces the radiation risk because it decreases use of X-ray imaging. In addition, this navigation method can also provide accurate 3D catheter shape information for endovascular surgery.

IJCAI Conference 2019 Conference Paper

Discriminative Sample Generation for Deep Imbalanced Learning

• Ting Guo
• Xingquan Zhu
• Yang Wang
• Fang Chen

In this paper, we propose a discriminative variational autoencoder (DVAE) to assist deep learning from data with imbalanced class distributions. DVAE is designed to alleviate the class imbalance by explicitly learning class boundaries between training samples, and uses learned class boundaries to guide the feature learning and sample generation. To learn class boundaries, DVAE learns a latent two-component mixture distributor, conditioned by the class labels, so the latent features can help differentiate minority class vs. majority class samples. In order to balance the training data for deep learning to emphasize on the minority class, we combine DVAE and generative adversarial networks (GAN) to form a unified model, DVAAN, which generates synthetic instances close to the class boundaries as training data to learn latent features and update the model. Experiments and comparisons confirm that DVAAN significantly alleviates the class imbalance and delivers accurate models for deep learning from imbalanced data.

JBHI Journal 2019 Journal Article

Three-Dimensional Feature-Enhanced Network for Automatic Femur Segmentation

• Fang Chen
• Jia Liu
• Zhe Zhao
• Mingyu Zhu
• Hongen Liao

Automatic femur segmentation from computed tomography volume is a crucial but challenging task for computer-aided diagnosis in orthopedic surgeries. The main obstacles are weak bone boundaries, narrowness of joint space, variations in femur density and shape, as well as diverse leg postures. In this paper, we presented a novel 3-D feature-enhanced network to address these challenges. The novelty of our approach lies in two feature enhancement modules, including the edge detection task and the multi-scale features fusion. First, the edge detection task was embedded into femur segmentation from computed tomography volume to solve the problems of narrow joint space and weak femur boundary. Crucially, a task-specific edge detector was used to optimize the performance of femur segmentation in an end-to-end trainable system. Second, the multi-scale features fusion provided both local and global contexts to handle the problems of large variations in leg postures as well as femur shape and density. The results demonstrated that accurate 3-D femur segmentation with a high Dice similarity coefficient of 96. 88% was achieved using the developed method, and the segmentation of computed tomography volume took 0. 93 s on an average.

JBHI Journal 2018 Journal Article

Clustering of Morphological Features for Identifying Femur Cavity Subtypes With Difficulties of Intramedullary Nail Implantation

• Fang Chen
• Zhe Zhao
• Cong Gao
• Jia Liu
• Xiuyun Su
• Jingxin Zhao
• Peifu Tang
• Hongen Liao

Intramedullary (IM) nail implantation is currently the standard treatment for femoral intertrochanteric fractures. However, individual differences in femur cavity bring a challenge in designing well-matched IM nails and cause difficulties in IM nail implantation. Therefore, there is an intense need to analyze femur cavities to predict difficulties in IM nail implantation to assist the design of IM nails. This study proposed a method to automatically identify subtypes of femur cavities that exhibit differences in potential difficulties in nail implantation by clustering the morphological features of femur models. The unsupervised subtype extraction method offers a scientific approach to stratify patients for designing and choosing well-matched IM nails. First, the quantitative morphological features of 422 femur cavities were extracted from computed tomography patient models. Second, 422 femur cavities were clustered into three distinct subtypes using a density peak-based k-means clustering method to provide a possible solution for the scientific design of IM nails. The effectiveness of the identified subtypes was validated by comparing subtype differences associated with IM nail implantation and the natural attributes of the patient. Quantitative evaluation of the mismatch degree and real clinical cases confirmed that the clustering results were clinically effective, with clear differences in the subtypes. Therefore, particular IM nails designed from the identified subtypes will potentially facilitate IM nail implantation and reduce complications. Compared with state-of-the-art methods, we used the largest scale dataset and unsupervised clustering to achieve subtype identification of femur cavities with clinical significance.

IJCAI Conference 2017 Conference Paper

Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process

• Ling Luo
• Bin Li
• Irena Koprinska
• Shlomo Berkovsky
• Fang Chen

Customer behavior modeling is important for businesses in order to understand, attract and retain customers. It is critical that the models are able to track the dynamics of customer behavior over time. We propose FC-CSM, a Customer Segmentation Model based on a Fragmentation-Coagulation process, which can track the evolution of customer segmentation, including the splitting and merging of customer groups. We conduct a case study using transaction data from a major Australian supermarket chain, where we: 1) show that our model achieves high fitness of purchase rate, outperforming models using mixture of Poisson processes; 2) compare the impact of promotions on customers for different products; and 3) track how customer groups evolve over time and how individual customers shift across groups. Our model provides valuable information to stakeholders about the different types of customers, how they change purchase behavior, and which customers are more receptive to promotion campaigns.

IJCAI Conference 2016 Conference Paper

Bayesian Optimization of Partition Layouts for Mondrian Processes

• Yi Wang
• Bin Li
• Xuhui Fan
• Yang Wang
• Fang Chen

The Mondrian process (MP) produces hierarchical partitions on a product space as a kd-tree, which can be served as a flexible yet parsimonious partition prior for relational modeling. Due to the recursive generation of partitions and varying dimensionality of the partition state space, the inference procedure for the MP relational modeling is extremely difficult. The prevalent inference method reversible-jump MCMC for this problem requires a number of unnecessary retrospective steps to transit from one partition state to a very similar one and it is prone to fall into a local optimum. In this paper, we attempt to circumvent these drawbacks by proposing an alternative method for inferring the MP partition structure. Based on the observation that similar cutting rate measures on the partition space lead to similar partition layouts, we propose to impose a nonhomogeneous cutting rate measure on the partition space to control the layouts of the generated partitions - the original MCMC sampling problem is thus transformed into a Bayesian global optimization problem. The empirical tests demonstrate that Bayesian optimization is able to find better partition structures than MCMC sampling with the same number of partition structure proposals.

NeurIPS Conference 2016 Conference Paper

Infinite Hidden Semi-Markov Modulated Interaction Point Process

• matt zhang
• Peng Lin
• Ting Guo
• Yang Wang
• Fang Chen

The correlation between events is ubiquitous and important for temporal events modelling. In many cases, the correlation exists between not only events' emitted observations, but also their arrival times. State space models (e. g. , hidden Markov model) and stochastic interaction point process models (e. g. , Hawkes process) have been studied extensively yet separately for the two types of correlations in the past. In this paper, we propose a Bayesian nonparametric approach that considers both types of correlations via unifying and generalizing hidden semi-Markov model and interaction point process model. The proposed approach can simultaneously model both the observations and arrival times of temporal events, and determine the number of latent states from data. A Metropolis-within-particle-Gibbs sampler with ancestor resampling is developed for efficient posterior inference. The approach is tested on both synthetic and real-world data with promising outcomes.

AAAI Conference 2016 Conference Paper

Interaction Point Processes via Infinite Branching Model

• Peng Lin
• Bang Zhang
• Ting Guo
• Yang Wang
• Fang Chen

Many natural and social phenomena can be modeled by interaction point processes (IPPs) (Diggle et al. 1994), stochastic point processes considering the interaction between points. In this paper, we propose the infinite branching model (IBM), a Bayesian statistical model that can generalize and extend some popular IPPs, e. g. , Hawkes process (Hawkes 1971; Hawkes and Oakes 1974). It treats IPP as a mixture of basis point processes with the aid of a distance dependent prior over branching structure that describes the relationship between points. The IBM can estimate point event intensity, interaction mechanism and branching structure simultaneously. A generic Metropolis-within-Gibbs sampling method is also developed for model parameter inference. The experiments on synthetic and real-world data demonstrate the superiority of the IBM.

AAAI Conference 2016 Conference Paper

The Ostomachion Process

• Xuhui Fan
• Bin Li
• Yi Wang
• Yang Wang
• Fang Chen

Stochastic partition processes for exchangeable graphs produce axis-aligned blocks on a product space. In relational modeling, the resulting blocks uncover the underlying interactions between two sets of entities of the relational data. Although some flexible axis-aligned partition processes, such as the Mondrian process, have been able to capture complex interacting patterns in a hierarchical fashion, they are still in short of capturing dependence between dimensions. To overcome this limitation, we propose the Ostomachion process (OP), which relaxes the cutting direction by allowing for oblique cuts. The partitions generated by an OP are convex polygons that can capture inter-dimensional dependence. The OP also exhibits interesting properties: 1) Along the time line the cutting times can be characterized by a homogeneous Poisson process, and 2) on the partition space the areas of the resulting components comply with a Dirichlet distribution. We can thus control the expected number of cuts and the expected areas of components through hyper-parameters. We adapt the reversible-jump MCMC algorithm for inferring OP partition structures. The experimental results on relational modeling and decision tree classification have validated the merit of the OP.

STOC Conference 1999 Conference Paper

Lifting Markov Chains to Speed up Mixing

• Fang Chen
• László Lovász 0001
• Igor Pak