Research Topics

(Updated Oct 2024)

Full publications on Google Scholar, DPLB, arXiv.

Research Summary: The research of my lab is focused on the principles and practice of machine intelligence, often with a focus on generalization, and making machine learning more reliable. Our applied research includes applications to healthcare, biomedical imaging, and cognitive neuroscience.

^‡ indicates authors with equal contribution. ^☆ indicates authors working closely with me.

(Robust) Machine Learning for Imperfect Data

The development of machine learning models, particularly in the context of label scarcity, increasingly necessitates the collection of substantial annotated data. Moreover, massive data often display a long-tailed class distribution or subpopulation shifts, which consequently results in notable imbalance issues. To this end, there are several growing interests in training machine learning models jointly across imbalanced subpopulation distributions and limited annotations. We are developing novel algorithmic and computational approaches to ensure the efficiency and robustness of large machine learning models. Our applied research includes applications to healthcare, biomedical imaging, and cognitive neuroimaging.

Calibrating Multi-modal Representations: A Pursuit of Group Robustness without Annotations
Chenyu You^‡, Yifei Min^‡, Weicheng Dai^‡, Jasjeet S. Sekhon, Lawrence Staib, James S. Duncan
IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2024
[preprint] / [code]
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Chenyu You^‡, Weicheng Dai^‡, Fenglin Liu, Yifei Min, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
[preprint] / [code]
Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S. Kevin Zhou, Lawrence Staib, James S. Duncan
Advances in Neural Information Processing Systems (NeurIPS), 2023
[preprint] / [code] / [news]
Bootstrapping Semi-supervised Medical Image Segmentation with Anatomical-aware Contrastive Distillation
Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan
Information Processing in Medical Imaging (IPMI), 2023
[preprint] / [code]
SimCVD: Simple Contrastive Voxel-Wise Representation Distillation for Semi-Supervised Medical Image Segmentation
Chenyu You, Yuan Zhou, Ruihan Zhao, Lawrence Staib, James S. Duncan
IEEE Transactions on Medical Imaging (TMI), 2022
[preprint]

Learning with Theoretical Guarantees

As machine learning methods have become ubiquitous in human decision-making, their reliability and interpretability have become important. This is particularly crucial in domains where decisions carry significant consequences, interpretable models can uncover crucial but unexpected patterns that complex models often obscure. We are currently studying provably interpretable modeling with theoretical guarantees. We are also exploring structured sparsity and attention in deep neural networks to enable interpretability.

Rethinking Semi-Supervised Medical Image Segmentation: A Variance-Reduction Perspective
Chenyu You, Weicheng Dai, Yifei Min, Fenglin Liu, David A. Clifton, S. Kevin Zhou, Lawrence Staib, James S. Duncan
Advances in Neural Information Processing Systems (NeurIPS), 2023
[preprint] / [code] / [news]
ACTION++: Improving Semi-supervised Medical Image Segmentation with Adaptive Anatomical Contrast
Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, Jasjeet S. Sekhon, James S. Duncan
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023 [Early Accept]
[preprint] / [code]
Class-Aware Adversarial Transformers for Medical Image Segmentation
Chenyu You, Ruihan Zhao, Fenglin Liu, Siyuan Dong, Sandeep Chinchali, Ufuk Topcu, Lawrence Staib, James S. Duncan
Advances in Neural Information Processing Systems (NeurIPS), 2022
[preprint] / [news]
Mine yOur owN Anatomy: Revisiting Medical Image Segmentation with Extremely Limited Labels
Chenyu You^‡, Weicheng Dai^‡, Fenglin Liu, Yifei Min, Xiaoxiao Li, David A. Clifton, Lawrence Staib, James S. Duncan
IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2024
[preprint] / [code]

Learning with Multi-Modality Data

Multi-modality data is ubiquitous in healthcare and science applications. We are pursuing various techniques for modeling such multiple data, primarily using probabilistic graphical models and other statistical analyses. These tools are primarily used to facilitate biomedical research. We are developing various tools to effectively tackle real-world challenges associated with data heterogeneity. Of particular interest are novel methods that address robustness issues, such as confounding, as well as trustworthy computational approaches, with primary applications in healthcare, biomedical imaging, and cognitive neuroscience.

End-to-end Spoken Conversational Question Answering: Task, Dataset and Model
Chenyu You^‡, Nuo Chen^‡, Fenglin Liu, Shen Ge, Xian Wu, Yuexian Zou
Findings of the North American Chapter of the Association for Computational Linguistics (NAACL), 2022
[preprint] / [code]
Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation
Fenglin Liu^☆, Chenyu You, Xian Wu, Shen Ge, Sheng Wang, Xu Sun
Advances in Neural Information Processing Systems (NeurIPS), 2021
[preprint]
Self-supervised Contrastive Cross-Modality Representation Learning for Spoken Question Answering
Chenyu You^‡, Nuo Chen^‡, Yuexian Zou
Findings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), 2021
[preprint] / [code]

Foundation Models for Biomedical Data

The development of medical foundation models often requires massive and diverse biomedical data. To this end, I have developed various foundation models for biomedical imaging data and explored novel applications of these models. I have also developed novel biomedical AI Agents that lead to the scalable and accurate predictive modeling, particularly for distribution shift problems.