2026
T²PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning
Haixin Wang, Hejie Cui#, Chenwei Zhang, Xin Liu, Shuowei Jin, Shijie Geng, Xinyang Zhang, Nasser Zalmout, Zhenyu Shi, Yizhou Sun (# corresponding author)
The International Conference on Machine Learning (ICML) 2026 Spotlight
Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization techniques such as fine-grained credit assignment and trajectory filtering, instability remains pervasive and often leads to training collapse. We argue that this instability stems from inefficient exploration in multi-turn settings, where po
T²PO: Uncertainty-Guided Exploration Control for Stable Multi-Turn Agentic Reinforcement Learning
Haixin Wang, Hejie Cui#, Chenwei Zhang, Xin Liu, Shuowei Jin, Shijie Geng, Xinyang Zhang, Nasser Zalmout, Zhenyu Shi, Yizhou Sun (# corresponding author)
The International Conference on Machine Learning (ICML) 2026 Spotlight
Recent progress in multi-turn reinforcement learning (RL) has significantly improved reasoning LLMs' performances on complex interactive tasks. Despite advances in stabilization techniques such as fine-grained credit assignment and trajectory filtering, instability remains pervasive and often leads to training collapse. We argue that this instability stems from inefficient exploration in multi-turn settings, where po
CoMem: Context Management with A Decoupled Long-Context Model
Yuwei Zhang, Chengyu Dong, Shuowei Jin, Changlong Yu, Hejie Cui, Hongye Jin, Xinyang Zhang, Hamed Bonab, Colin Lockard, Jianshu Chen, Zhenyu Shi, Jingbo Shang, Xian Li, Bing Yin
The International Conference on Machine Learning (ICML) 2026
Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this process typically incurs substantial decoding overhead for the extra summarization tokens, which significantly affect the end-to-end response latency at deployment. In this paper, we introduce CoMem, a novel framework that decouples memory management from the primary a
CoMem: Context Management with A Decoupled Long-Context Model
Yuwei Zhang, Chengyu Dong, Shuowei Jin, Changlong Yu, Hejie Cui, Hongye Jin, Xinyang Zhang, Hamed Bonab, Colin Lockard, Jianshu Chen, Zhenyu Shi, Jingbo Shang, Xian Li, Bing Yin
The International Conference on Machine Learning (ICML) 2026
Context management enables agentic models to solve long-horizon tasks through iterative summarization of previous interaction histories. However, this process typically incurs substantial decoding overhead for the extra summarization tokens, which significantly affect the end-to-end response latency at deployment. In this paper, we introduce CoMem, a novel framework that decouples memory management from the primary a
EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
Yuzhang Xie, Keqi Han, Yunpeng Xiao, Hejie Cui, Guanchen Wu, Ziyang Zhang, Kai Shu, Jiaying Lu, Xiao Hu, Carl Yang
The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Datasets and Benchmarks Track 2026 Oral
Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence. EHRBench is an automated and reliable EHR-grounded benchmark for evaluating LLM-based clinical decision-making at scale. It constructs nearly 1M QA items spanning diagnosis, treatment, and prognosis, and benchmarks more than 30 representative LLMs to reveal actionable gaps toward clinically reliable LLM systems.
EHRBench: An Automated and Reliable EHR-based Benchmark for Clinical Decision Making with LLMs
Yuzhang Xie, Keqi Han, Yunpeng Xiao, Hejie Cui, Guanchen Wu, Ziyang Zhang, Kai Shu, Jiaying Lu, Xiao Hu, Carl Yang
The ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), Datasets and Benchmarks Track 2026 Oral
Clinical decision-making (CDM) is central to real-world clinical workflows, where clinicians infer diagnoses, select treatments, or anticipate future health outcomes under incomplete evidence. EHRBench is an automated and reliable EHR-grounded benchmark for evaluating LLM-based clinical decision-making at scale. It constructs nearly 1M QA items spanning diagnosis, treatment, and prognosis, and benchmarks more than 30 representative LLMs to reveal actionable gaps toward clinically reliable LLM systems.
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
Suhana Bedi*, Hejie Cui*, Miguel Fuentes*, Alyssa Unell*, Michael Wornow, Juan M. Banda, Nikesh Kotecha, Timothy Keyes, Yifan Mai, Mert Oez, Hao Qiu, Shrey Jain, Leonardo Schettini, Mehr Kashyap, Jason Alan Fries, Akshay Swaminathan, Philip Chung, Fateme Nateghi Haredasht, Ivan Lopez, Asad Aali, Gabriel Tse, Ashwin Nayak, Shivam Vedak, Sneha S. Jain, Birju Patel, Oluseyi Fayanju, Shreya Shah, Ethan Goh, Dong-han Yao, Brian Soetikno, Eduardo Reis, Sergios Gatidis, Vasu Divi, Robson Capasso, Rachna Saralkar, Chia-Chun Chiang, Jenelle Jindal, Tho Pham, Faraz Ghoddusi, Steven Lin, Albert S. Chiou, Christy Hong, Mohana Roy, Michael F. Gensheimer, Hinesh Patel, Kevin Schulman, Dev Dash, Danton Char, Lance Downing, Francois Grolleau, Kameron Black, Bethel Mieso, Aydin Zahedivash, Wen-wai Yim, Harshita Sharma, Tony Lee, Hannah Kirsch, Jennifer Lee, Nerissa Ambers, Carlene Lugtu, Aditya Sharma, Bilal Mawji, Alex Alekseyev, Vicky Zhou, Vikas Kakkar, Jarrod Helzer, Anurang Revri, Yair Bannett, Roxana Daneshjou, Jonathan Chen, Emily Alsentzer, Keith Morse, Nirmal Ravi, Nima Aghaeepour, Vanessa Kennedy, Akshay Chaudhari, Thomas Wang, Sanmi Koyejo, Matthew P. Lungren, Eric Horvitz, Percy Liang, Michael A. Pfeffer, Nigam H. Shah (* equal contribution)
Nature Medicine (5-Year Impact Factor: 52.4) 2026
Large language models (LLMs) achieve near-perfect scores on medical licensing exams, yet these benchmarks fail to capture the complexity of real-world clinical practice. MedHELM addresses this gap by introducing a clinician-validated taxonomy of five categories, 22 subcategories, and 121 medical tasks; a suite of 37 benchmarks (including real-world EHR datasets); and an improved evaluation methodology leveraging an L
MedHELM: Holistic Evaluation of Large Language Models for Medical Tasks
Suhana Bedi*, Hejie Cui*, Miguel Fuentes*, Alyssa Unell*, Michael Wornow, Juan M. Banda, Nikesh Kotecha, Timothy Keyes, Yifan Mai, Mert Oez, Hao Qiu, Shrey Jain, Leonardo Schettini, Mehr Kashyap, Jason Alan Fries, Akshay Swaminathan, Philip Chung, Fateme Nateghi Haredasht, Ivan Lopez, Asad Aali, Gabriel Tse, Ashwin Nayak, Shivam Vedak, Sneha S. Jain, Birju Patel, Oluseyi Fayanju, Shreya Shah, Ethan Goh, Dong-han Yao, Brian Soetikno, Eduardo Reis, Sergios Gatidis, Vasu Divi, Robson Capasso, Rachna Saralkar, Chia-Chun Chiang, Jenelle Jindal, Tho Pham, Faraz Ghoddusi, Steven Lin, Albert S. Chiou, Christy Hong, Mohana Roy, Michael F. Gensheimer, Hinesh Patel, Kevin Schulman, Dev Dash, Danton Char, Lance Downing, Francois Grolleau, Kameron Black, Bethel Mieso, Aydin Zahedivash, Wen-wai Yim, Harshita Sharma, Tony Lee, Hannah Kirsch, Jennifer Lee, Nerissa Ambers, Carlene Lugtu, Aditya Sharma, Bilal Mawji, Alex Alekseyev, Vicky Zhou, Vikas Kakkar, Jarrod Helzer, Anurang Revri, Yair Bannett, Roxana Daneshjou, Jonathan Chen, Emily Alsentzer, Keith Morse, Nirmal Ravi, Nima Aghaeepour, Vanessa Kennedy, Akshay Chaudhari, Thomas Wang, Sanmi Koyejo, Matthew P. Lungren, Eric Horvitz, Percy Liang, Michael A. Pfeffer, Nigam H. Shah (* equal contribution)
Nature Medicine (5-Year Impact Factor: 52.4) 2026
Large language models (LLMs) achieve near-perfect scores on medical licensing exams, yet these benchmarks fail to capture the complexity of real-world clinical practice. MedHELM addresses this gap by introducing a clinician-validated taxonomy of five categories, 22 subcategories, and 121 medical tasks; a suite of 37 benchmarks (including real-world EHR datasets); and an improved evaluation methodology leveraging an L
2025
TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records
Hejie Cui*, Alyssa Unell*, Bowen Chen, Jason Alan Fries, Emily Alsentzer, Sanmi Koyejo, Nigam H. Shah (* equal contribution)
npj Digital Medicine (5-Year Impact Factor: 17.0) 2025
Electronic health records (EHRs) contain rich longitudinal information essential for clinical decision-making, yet large language models (LLMs) struggle to reason across patient timelines. We introduce \textbf{TIMER} (\textbf{T}emporal \textbf{I}nstruction \textbf{M}odeling and \textbf{E}valuation for Longitudinal Clinical \textbf{R}ecords), a method to improve LLMs’ temporal reasoning over multi-visit EHRs through t
TIMER: Temporal Instruction Modeling and Evaluation for Longitudinal Clinical Records
Hejie Cui*, Alyssa Unell*, Bowen Chen, Jason Alan Fries, Emily Alsentzer, Sanmi Koyejo, Nigam H. Shah (* equal contribution)
npj Digital Medicine (5-Year Impact Factor: 17.0) 2025
Electronic health records (EHRs) contain rich longitudinal information essential for clinical decision-making, yet large language models (LLMs) struggle to reason across patient timelines. We introduce \textbf{TIMER} (\textbf{T}emporal \textbf{I}nstruction \textbf{M}odeling and \textbf{E}valuation for Longitudinal Clinical \textbf{R}ecords), a method to improve LLMs’ temporal reasoning over multi-visit EHRs through t
CuraBench: A Benchmark Dataset Generation System for Healthcare AI Evaluation
Hejie Cui, Alyssa Unell, Haoran Zhang, Caleb Winston, Jason Alan Fries, Sanmi Koyejo, Nigam H. Shah
KDD 2025 HealthDay Blue Sky Ideas Track 2025
Ensuring that artificial intelligence (AI) tools in healthcare operate safely and effectively requires robust evaluation within realistic clinical contexts. Traditional evaluation methods often rely on standardized benchmarks that fail to capture the full complexity of patient care, while manually curating a dataset for a specific deployment scenario can be time-consuming and limiting. We propose CuraBench, a configu
CuraBench: A Benchmark Dataset Generation System for Healthcare AI Evaluation
Hejie Cui, Alyssa Unell, Haoran Zhang, Caleb Winston, Jason Alan Fries, Sanmi Koyejo, Nigam H. Shah
KDD 2025 HealthDay Blue Sky Ideas Track 2025
Ensuring that artificial intelligence (AI) tools in healthcare operate safely and effectively requires robust evaluation within realistic clinical contexts. Traditional evaluation methods often rely on standardized benchmarks that fail to capture the full complexity of patient care, while manually curating a dataset for a specific deployment scenario can be time-consuming and limiting. We propose CuraBench, a configu
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
Hejie Cui*, Jiaying Lu*, Ran Xu*, Shiyu Wang, Wenjing Ma, Yue Yu, Shaojun Yu, Xuan Kan, Chen Ling, Liang Zhao, Zhaohui S. Qin, Joyce C. Ho, Tianfan Fu, Jing Ma, Mengdi Huai, Fei Wang, Carl Yang (* equal contribution)
Journal of Biomedical Informatics (JBI) (IF: 4.5) 2025
Objective: This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. Methods: We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science researc
A Review on Knowledge Graphs for Healthcare: Resources, Applications, and Promises
Hejie Cui*, Jiaying Lu*, Ran Xu*, Shiyu Wang, Wenjing Ma, Yue Yu, Shaojun Yu, Xuan Kan, Chen Ling, Liang Zhao, Zhaohui S. Qin, Joyce C. Ho, Tianfan Fu, Jing Ma, Mengdi Huai, Fei Wang, Carl Yang (* equal contribution)
Journal of Biomedical Informatics (JBI) (IF: 4.5) 2025
Objective: This comprehensive review aims to provide an overview of the current state of Healthcare Knowledge Graphs (HKGs), including their construction, utilization models, and applications across various healthcare and biomedical research domains. Methods: We thoroughly analyzed existing literature on HKGs, covering their construction methodologies, utilization techniques, and applications in basic science researc
CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models
Wei Dai, Peilin Chen, Malinda Lu, Daniel Li, Haowen Wei, Hejie Cui, Paul Pu Liang
The International Conference on Machine Learning (ICML) 2025
Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the development of large-scale multimodal methods that can make holistic assessments of patient health and well-being. To bridge this gap, we introduce Clinical Large-Scale Integrative Multimodal Benchmark (CLI
CLIMB: Data Foundations for Large Scale Multimodal Clinical Foundation Models
Wei Dai, Peilin Chen, Malinda Lu, Daniel Li, Haowen Wei, Hejie Cui, Paul Pu Liang
The International Conference on Machine Learning (ICML) 2025
Recent advances in clinical AI have enabled remarkable progress across many clinical domains. However, existing benchmarks and models are primarily limited to a small set of modalities and tasks, which hinders the development of large-scale multimodal methods that can make holistic assessments of patient health and well-being. To bridge this gap, we introduce Clinical Large-Scale Integrative Multimodal Benchmark (CLI
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
Hejie Cui, Xinyu Fang, Ran Xu, Xuan Kan, Joyce C. Ho, Carl Yang
International Medical Informatics Conference (MedInfo) 2025
Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous information, such as structural data in tabular form and unstructured data in textual notes. Different types of information in EHRs can complement each other and provide a more complete picture of the health status of a patient. While there has been
Multimodal Fusion of EHR in Structures and Semantics: Integrating Clinical Records and Notes with Hypergraph and LLM
Hejie Cui, Xinyu Fang, Ran Xu, Xuan Kan, Joyce C. Ho, Carl Yang
International Medical Informatics Conference (MedInfo) 2025
Electronic Health Records (EHRs) have become increasingly popular to support clinical decision-making and healthcare in recent decades. EHRs usually contain heterogeneous information, such as structural data in tabular form and unstructured data in textual notes. Different types of information in EHRs can complement each other and provide a more complete picture of the health status of a patient. While there has been
2024
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Hejie Cui*, Lingjun Mao*, Xin Liang, Jieyu Zhang, Hui Ren, Quanzheng Li, Xiang Li, Carl Yang (* equal contribution)
The Conference on Neural Information Processing Systems (NeurIPS) 2024
Recent advancements in multimodal foundation models have showcased impressive capabilities in understanding and reasoning with visual and textual information. Adapting these foundation models trained for general usage to specialized domains like biomedicine requires large-scale domain-specific instruction datasets. While existing works have explored curating such datasets automatically, the resultant datasets are not
Biomedical Visual Instruction Tuning with Clinician Preference Alignment
Hejie Cui*, Lingjun Mao*, Xin Liang, Jieyu Zhang, Hui Ren, Quanzheng Li, Xiang Li, Carl Yang (* equal contribution)
The Conference on Neural Information Processing Systems (NeurIPS) 2024
Recent advancements in multimodal foundation models have showcased impressive capabilities in understanding and reasoning with visual and textual information. Adapting these foundation models trained for general usage to specialized domains like biomedicine requires large-scale domain-specific instruction datasets. While existing works have explored curating such datasets automatically, the resultant datasets are not
Microstructures and Accuracy of Graph Recall by Large Language Models
Yanbang Wang, Hejie Cui, Jon Kleinberg
The Conference on Neural Information Processing Systems (NeurIPS) 2024 IC2S2 Oral
Graphs data is crucial for many applications, and much of it exists in the relations described in textual format. As a result, being able to accurately recall and encode a graph described in earlier text is a basic yet pivotal ability that LLMs need to demonstrate if they are to perform reasoning tasks that involve graph-structured information. Human performance at graph recall by has been studied by cognitive scient
Microstructures and Accuracy of Graph Recall by Large Language Models
Yanbang Wang, Hejie Cui, Jon Kleinberg
The Conference on Neural Information Processing Systems (NeurIPS) 2024 IC2S2 Oral
Graphs data is crucial for many applications, and much of it exists in the relations described in textual format. As a result, being able to accurately recall and encode a graph described in earlier text is a basic yet pivotal ability that LLMs need to demonstrate if they are to perform reasoning tasks that involve graph-structured information. Human performance at graph recall by has been studied by cognitive scient
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Hejie Cui, Zhuocheng Shen, Jieyu Zhang, Hui Shao, Lianhui Qin, Joyce C Ho, Carl Yang
American Medical Informatics Association (AMIA) Annual Symposium (Oral) 2024 Oral
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain. In this study, we investigate the feasibility of applying Large Language Models (LLMs) to convert structured patient visit data (e.g., diagnoses,
LLMs-based Few-Shot Disease Predictions using EHR: A Novel Approach Combining Predictive Agent Reasoning and Critical Agent Instruction
Hejie Cui, Zhuocheng Shen, Jieyu Zhang, Hui Shao, Lianhui Qin, Joyce C Ho, Carl Yang
American Medical Informatics Association (AMIA) Annual Symposium (Oral) 2024 Oral
Electronic health records (EHRs) contain valuable patient data for health-related prediction tasks, such as disease prediction. Traditional approaches rely on supervised learning methods that require large labeled datasets, which can be expensive and challenging to obtain. In this study, we investigate the feasibility of applying Large Language Models (LLMs) to convert structured patient visit data (e.g., diagnoses,
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
Ziyang Zhang, Hejie Cui, Ran Xu, Yuzhang Xie, Joyce C Ho, Carl Yang
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2024
The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of complex diseases, failing to model the potential disease subtypes regarding their corresponding patient visits and clinical concept subgroups. In this work, we introduce TA
TACCO: Task-guided Co-clustering of Clinical Concepts and Patient Visits for Disease Subtyping based on EHR Data
Ziyang Zhang, Hejie Cui, Ran Xu, Yuzhang Xie, Joyce C Ho, Carl Yang
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2024
The growing availability of well-organized Electronic Health Records (EHR) data has enabled the development of various machine learning models towards disease risk prediction. However, existing risk prediction methods overlook the heterogeneity of complex diseases, failing to model the potential disease subtypes regarding their corresponding patient visits and clinical concept subgroups. In this work, we introduce TA
Brain Network Analysis with Graph Neural Network
Hejie Cui, Xuan Kan, Xiaoxiao Li, Ying Guo, Lifang He, Liang Zhan, Carl Yang
Tutorial at the IEEE International Symposium on Biomedical Imaging (ISBI) 2024
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic
Brain Network Analysis with Graph Neural Network
Hejie Cui, Xuan Kan, Xiaoxiao Li, Ying Guo, Lifang He, Liang Zhan, Carl Yang
Tutorial at the IEEE International Symposium on Biomedical Imaging (ISBI) 2024
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic
2023
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce C Ho, Carl Yang
Annual Meeting of the Association for Computational Linguistics (ACL-Findings) 2023
Clinical natural language processing requires methods that can address domainspecific challenges, such as complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources. To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical
Knowledge-Infused Prompting: Assessing and Advancing Clinical Text Data Generation with Large Language Models
Ran Xu, Hejie Cui, Yue Yu, Xuan Kan, Wenqi Shi, Yuchen Zhuang, Wei Jin, Joyce C Ho, Carl Yang
Annual Meeting of the Association for Computational Linguistics (ACL-Findings) 2023
Clinical natural language processing requires methods that can address domainspecific challenges, such as complex medical terminology and clinical contexts. Recently, large language models (LLMs) have shown promise in this domain. Yet, their direct deployment can lead to privacy issues and are constrained by resources. To address this challenge, we delve into synthetic clinical text generation using LLMs for clinical
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Hejie Cui*, Xinyu Fang*, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Manling Li, Yangqiu Song, Carl Yang (* equal contribution)
The Conference on Neural Information Processing Systems (NeurIPS) 2023
Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a first exploration to a new paradigm of open visual knowledge extraction. To achieve this, we present
Open Visual Knowledge Extraction via Relation-Oriented Multimodality Model Prompting
Hejie Cui*, Xinyu Fang*, Zihan Zhang, Ran Xu, Xuan Kan, Xin Liu, Manling Li, Yangqiu Song, Carl Yang (* equal contribution)
The Conference on Neural Information Processing Systems (NeurIPS) 2023
Images contain rich relational knowledge that can help machines understand the world. Existing methods on visual knowledge extraction often rely on the pre-defined format (e.g., sub-verb-obj tuples) or vocabulary (e.g., relation types), restricting the expressiveness of the extracted knowledge. In this work, we take a first exploration to a new paradigm of open visual knowledge extraction. To achieve this, we present
R-Mixup: Riemannian Mixup for Biological Networks
Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, Ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2023
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-Mixup, a Mixup-based data au
R-Mixup: Riemannian Mixup for Biological Networks
Xuan Kan, Zimu Li, Hejie Cui, Yue Yu, Ran Xu, Shaojun Yu, Zilong Zhang, Ying Guo, Carl Yang
ACM SIGKDD International Conference on Knowledge Discovery and Data Mining 2023
Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-Mixup, a Mixup-based data au
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li
Annual Meeting of the Association for Computational Linguistics (ACL-Findings) 2023
Attribute value extraction, as a fundamental task in e-Commerce services, has been extensively studied and formulated as text-based extraction. However, many attributes can benefit from image-based extraction, like the product color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the t
PV2TEA: Patching Visual Modality to Textual-Established Information Extraction
Hejie Cui, Rongmei Lin, Nasser Zalmout, Chenwei Zhang, Jingbo Shang, Carl Yang, Xian Li
Annual Meeting of the Association for Computational Linguistics (ACL-Findings) 2023
Attribute value extraction, as a fundamental task in e-Commerce services, has been extensively studied and formulated as text-based extraction. However, many attributes can benefit from image-based extraction, like the product color, shape, pattern, among others. The visual modality has long been underutilized, mainly due to multimodal annotation difficulty. In this paper, we aim to patch the visual modality to the t
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
Yi Yang, Hejie Cui, Carl Yang
The Conference on Health, Inference, and Learning (CHIL) 2023 Oral
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have shown a growing interest in the interactions between brain regions of interest (ROIs) and their impact on neural development and disorder diagnosis. As a powerful deep model for analyzing graph-structured data, Graph Neural Networks (GNNs)
PTGB: Pre-Train Graph Neural Networks for Brain Network Analysis
Yi Yang, Hejie Cui, Carl Yang
The Conference on Health, Inference, and Learning (CHIL) 2023 Oral
The human brain is the central hub of the neurobiological system, controlling behavior and cognition in complex ways. Recent advances in neuroscience and neuroimaging analysis have shown a growing interest in the interactions between brain regions of interest (ROIs) and their impact on neural development and disorder diagnosis. As a powerful deep model for analyzing graph-structured data, Graph Neural Networks (GNNs)
Brain Network Analysis with Graph Neural Network
Hejie Cui, Xuan Kan, Xiaoxiao Li, Lifang He, Liang Zhan, Ying Guo, Carl Yang
Tutorial at the International Conference on Intelligent Biology and Medicine (ICIBM) 2023
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic
Brain Network Analysis with Graph Neural Network
Hejie Cui, Xuan Kan, Xiaoxiao Li, Lifang He, Liang Zhan, Ying Guo, Carl Yang
Tutorial at the International Conference on Intelligent Biology and Medicine (ICIBM) 2023
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated by geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systematic
Neighborhood-regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C Ho, Chao Zhang, Carl Yang
The AAAI International Conference on Artificial Intelligence 2023 Oral
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar repr
Neighborhood-regularized Self-Training for Learning with Few Labels
Ran Xu, Yue Yu, Hejie Cui, Xuan Kan, Yanqiao Zhu, Joyce C Ho, Chao Zhang, Carl Yang
The AAAI International Conference on Artificial Intelligence 2023 Oral
Training deep neural networks (DNNs) with limited supervision has been a popular research topic as it can significantly alleviate the annotation burden. Self-training has been successfully applied in semi-supervised learning tasks, but one drawback of self-training is that it is vulnerable to the label noise from incorrect pseudo labels. Inspired by the fact that samples with similar labels tend to share similar repr
2022
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang
IEEE Transactions on Medical Imaging (5-Year Impact Factor: 12.3) 2022
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systemat
BrainGB: A Benchmark for Brain Network Analysis with Graph Neural Networks
Hejie Cui, Wei Dai, Yanqiao Zhu, Xuan Kan, Antonio Aodong Chen Gu, Joshua Lukemire, Liang Zhan, Lifang He, Ying Guo, Carl Yang
IEEE Transactions on Medical Imaging (5-Year Impact Factor: 12.3) 2022
Mapping the connectome of the human brain using structural or functional connectivity has become one of the most pervasive paradigms for neuroimaging analysis. Recently, Graph Neural Networks (GNNs) motivated from geometric deep learning have attracted broad interest due to their established power for modeling complex networked data. Despite their superior performance in many fields, there has not yet been a systemat
Brain Network Transformer
Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang
The Conference on Neural Information Processing Systems (NeurIPS) 2022 Spotlight
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data,
Brain Network Transformer
Xuan Kan, Wei Dai, Hejie Cui, Zilong Zhang, Ying Guo, Carl Yang
The Conference on Neural Information Processing Systems (NeurIPS) 2022 Spotlight
Human brains are commonly modeled as networks of Regions of Interest (ROIs) and their connections for the understanding of brain functions and mental disorders. Recently, Transformer-based models have been studied over different types of data, including graphs, shown to bring performance gains widely. In this work, we study Transformer-based models for brain network analysis. Driven by the unique properties of data,
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Hejie Cui, Zijie Lu, Pan Li, Carl Yang
The ACM International Conference on Information and Knowledge Management (CIKM) 2022 Most Influential CIKM Paper of 2022
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two ty
On Positional and Structural Node Features for Graph Neural Networks on Non-attributed Graphs
Hejie Cui, Zijie Lu, Pan Li, Carl Yang
The ACM International Conference on Information and Knowledge Management (CIKM) 2022 Most Influential CIKM Paper of 2022
Graph neural networks (GNNs) have been widely used in various graph-related problems such as node classification and graph classification, where the superior performance is mainly established when natural node features are available. However, it is not well understood how GNNs work without natural node features, especially regarding the various ways to construct artificial ones. In this paper, we point out the two ty
Interpretable GNNs for Connectome-Based Brain Disorder Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang
The International Conference on Medical Image Computing and Computer Assisted Intervention 2022 Oral
Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neur
Interpretable GNNs for Connectome-Based Brain Disorder Analysis
Hejie Cui, Wei Dai, Yanqiao Zhu, Xiaoxiao Li, Lifang He, Carl Yang
The International Conference on Medical Image Computing and Computer Assisted Intervention 2022 Oral
Human brains lie at the core of complex neurobiological systems, where the neurons, circuits, and subsystems interact in enigmatic ways. Understanding the structural and functional mechanisms of the brain has long been an intriguing pursuit for neuroscience research and clinical disorder therapy. Mapping the connections of the human brain as a network is one of the most pervasive paradigms in neuroscience. Graph Neur
FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang
The Medical Imaging with Deep Learning Conference 2022 Oral
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brainnetworks are noisy and unaware of down stream prediction tasks, while also incompatible with recent powerful machine learning models of GNNs. In this work, we develop an end-to-end trainable pipeline to extract prominent fMRI fe
FBNetGen: Task-aware GNN-based fMRI Analysis via Functional Brain Network Generation
Xuan Kan, Hejie Cui, Joshua Lukemire, Ying Guo, Carl Yang
The Medical Imaging with Deep Learning Conference 2022 Oral
Recent studies in neuroscience show great potential of functional brain networks constructed from fMRI data for popularity modeling and clinical predictions. However, existing functional brainnetworks are noisy and unaware of down stream prediction tasks, while also incompatible with recent powerful machine learning models of GNNs. In this work, we develop an end-to-end trainable pipeline to extract prominent fMRI fe
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Hejie Cui, Jiaying Lu, Yao Ge, Carl Yang
The European Conference on Information Retrieval 2022
Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured texts represented as concept maps, GNNs can be exploited for tasks like document retrieval. Intrigued by how can GNNs help document retrieval, we conduct an empirical study on a large-scale multi-discipline dataset CORD-19. Results show th
How Can Graph Neural Networks Help Document Retrieval: A Case Study on CORD19 with Concept Map Generation
Hejie Cui, Jiaying Lu, Yao Ge, Carl Yang
The European Conference on Information Retrieval 2022
Graph neural networks (GNNs), as a group of powerful tools for representation learning on irregular data, have manifested superiority in various downstream tasks. With unstructured texts represented as concept maps, GNNs can be exploited for tasks like document retrieval. Intrigued by how can GNNs help document retrieval, we conduct an empirical study on a large-scale multi-discipline dataset CORD-19. Results show th
2021
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
Xuan Kan, Hejie Cui, Carl Yang
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks. Existing SGG frameworks, however, require heavy training yet are incapable of modeling unseen (i.e.,zero-shot) triplets. In this work, we stress that such incapability is due to the lack of commonsense reasoning, i.e., the ability to associate simil
Zero-Shot Scene Graph Relation Prediction through Commonsense Knowledge Integration
Xuan Kan, Hejie Cui, Carl Yang
The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2021
Relation prediction among entities in images is an important step in scene graph generation (SGG), which further impacts various visual understanding and reasoning tasks. Existing SGG frameworks, however, require heavy training yet are incapable of modeling unseen (i.e.,zero-shot) triplets. In this work, we stress that such incapability is due to the lack of commonsense reasoning, i.e., the ability to associate simil
2019
Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Hejie Cui, Xinglong Liu, Ning Huang
The International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2019
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure. In this work, we present an effective framework and refinement process of pulmonary vessel segmentation from chest computed tomographic (CT) images. The key to our approach is a 2.5D segmentation network applied from three orthogonal axes, which presents a robust and ful
Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Hejie Cui, Xinglong Liu, Ning Huang
The International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2019
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure. In this work, we present an effective framework and refinement process of pulmonary vessel segmentation from chest computed tomographic (CT) images. The key to our approach is a 2.5D segmentation network applied from three orthogonal axes, which presents a robust and ful