Chenyu You – Research

Department of Electrical Engineering, Yale University

Full publications on Google Scholar.

Research Summary: My research interest is in Biomedical AI. Specifically, I works to develop the principles and practice of generalizable, robust, and trustworthy machine learning for healthcare. Some recent highlights include (i) robust machine learning for imperfect medical data, (ii) multi-modal biomedical data analytics, and (iii) natural language processing (NLP) in healthcare.

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

(Robust) Machine Learning for Imperfect Medical Data

The development of machine learning models, particularly in the context of label scarcity, increasingly necessitates the collection of substantial annotated medical data. Moreover, medical data often display a long-tailed class distribution, which consequently results in notable imbalance issues. To this end, there are several growing interests in training machine learning models jointly across imbalanced class distributions and limited annotations. I have developed novel, efficient, statistically consistent algorithms to improve empirical performance for biomedical image analysis.

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]
Momentum Contrastive Voxel-wise Representation Learning for Semi-supervised Volumetric Medical Image Segmentation
Chenyu You, Ruihan Zhao, Lawrence Staib, James S. Duncan
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2022 [Early Accept]
[preprint]
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 [Major Revision]
[preprint] / [code]

Learning with Theoretical Guarantees

As machine learning methods have become ubiquitous in clinical decision-making, their reliability and interpretability have become important. This is particularly crucial in the field of biomedical image analysis, where decision outcomes can have profound implications. I have developed novel machine learning algorithms that enable provably accurate anatomical modeling with theoretical guarantees.

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]

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 medical AI Agents that lead to the scalable and accurate predictive modeling, particularly for distribution shift problems.

Implicit Anatomical Rendering for Medical Image Segmentation with Stochastic Experts
Chenyu You, Weicheng Dai, Yifei Min, Lawrence Staib, James S. Duncan
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI), 2023 [Early Accept]
[preprint] / [code]
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]
Segment Anything in Medical Images
Jun Ma^☆, Yuting He^☆, Feifei Li, Lin Han, Chenyu You, Bo Wang
Nature Communication (Nat. Commun.), 2023
[preprint] / [paper] / [code]

Multi-Modal Biomedical Data Analytics

Real-world machine learning often requires multi-modality biomedical data for disease prevention, diagnosis, prognosis, and treatment design. I have developed multi-modality learning algorithms (e.g., multimodal foundation model), primarily through the use of foundation models and probabilistic graphical analyses. These tools are principally employed to facilitate clinical research. I have also developed novel methods that effectively tackle clinical challenges associated with data heterogeneity.

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]

Funding: I graciously acknowledge generous funding support from the National Institutes of Health, National Science Foundation, and the Yale Fellowships and Funding. My research is also supported by generous computing support from Yale Center for Research Computing, Meta AI, Google Cloud, and Amazon Web Services.