Cheng Chen

Assistant Professor

Department of Electrical and Electronic Engineering
The University of Hong Kong
Hong Kong

Office: Rm 720, Chow Yei Ching Building, HKU
Email: cchen at eee.hku.hk

Biography

I am currently an Assistant Professor at the Department of Electrical and Electronic Engineering, the University of Hong Kong. Before joining HKU, I was a postdoctoral research fellow at the Center for Advanced Medical Computing and Analysis, Harvard Medical School/Massachusetts General Hospital, working with Prof. Quanzheng Li. I received my Ph.D. from the Department of Computer Science and Engineering, The Chinese University of Hong Kong, supervised by Prof. Pheng-Ann Heng and Prof. Qi Dou. Previously, I received my M.S. from The Johns Hopkins University, and B.S. from Zhejiang University, both with a specialization in biomedical engineering.

My research interests lie in the intersection of artificial intelligence and healthcare, with an emphasis on the application in medical image analysis. The research topics I have explored include visual foundation models in medical imaging, cross-modal self-supervised learning, deep model generalization, and robust multi-modal learning.

*Opening!* I am actively looking for self-motivated Ph.D. students, research assistants, and visiting students to join my group. Please feel free to drop me an email with your CV and transcripts if you are interested.

If you are an HKU student interested in doing research with me, also feel free to drop me an email!

News

[09/2024] World’s Top 2% Scientists by Stanford University.
[08/2024] Our paper on modality-agnostic SAM adaptation was accepted by Medical Image Analysis Special Issue.
[05/2024] Three papers are accepted by MICCAI 2024 (two early accept).
[02/2024] Serve as Area Chair of MICCAI 2024.
[09/2023] Our paper on uncertainty estimation was accepted by NeurIPS 2023.
[07/2023] One paper on causality-inspired semi-supervised learning was accepted by ICCV 2023.
[05/2023] Our TMI paper on unsupervised domain adaptation has been identified as ESI highly cited paper.
[05/2023] Three papers are early accepted to MICCAI 2023 with one oral.
[03/2023] Paper on personalized federated learning in medical imaging was accepted by IEEE TMI.
[02/2023] Paper on diffusion model based brain midline shift quantification was accepted by IPMI.
[08/2022] Proud to receive the certificate of IEEE TMI Distinguished Reviewer.
[07/2022] Paper on dynamic learning rate for test-time adaptation was accepted by IEEE TMI.
[05/2022] Two papers on model generalization were early accepted by MICCAI'22.
[12/2021] Our paper on single domain generalization for segmentation was accepted by AAAI'22.
[10/2021] Paper on leveraing privileged multimodal knowledge for unimodal segmentation was accepted by IEEE TMI.
[09/2021] We won the champion of 2021 MICCAI Federated Brain Tumor Segmentation Challenge (FeTS).
[23/06/2021] Passed the oral defense and became a Dr.!
[05/2021] One paper on source-free unsupervised domain adaptation was early accepted by MICCAI'21.
[03/2021] Our paper on federated domain generalization (FedDG) was accepted by CVPR'21.
[03/2021] Our paper on relating temporal cues for surgical workflow recognition was accepted to IEEE TMI.
[02/2020] One paper on unsupervised domain adaptation was accepted by IEEE TMI.
[08/2019] One paper on robust multimodal learning was accepted by MICCAI'19, with a travel award.

Selected Publications [Google Scholar]

	MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation. Cheng Chen, Juzheng Miao, Dufan Wu, Zhiling Yan, Sekeun Kim, Jiang Hu, Aoxiao Zhong, Zhengliang Liu, Lichao Sun, Xiang Li, Tianming Liu, Pheng-Ann Heng, Quanzheng Li. Preprint, 2023. [paper][code]
	Contrastive Masked Image-Text Modeling for Medical Visual Representation Learning. Cheng Chen, Aoxiao Zhong, Dufan Wu, Jie Luo, Quanzheng Li. Medical Image Computing and Computer Assisted Interventions (MICCAI), 2023. (Oral) [paper][code]
	Uncertainty Estimation for Safety-critical Scene Segmentation via Fine-grained Reward Maximization. Hongzheng Yang, Cheng Chen, Yueyao Chen, Markus Scheppach, Hon Chi Yip, Qi Dou. Conference on Neural Information Processing Systems (NeurIPS), 2023. [paper][code] (* indicates equal contribution)
	DLTTA: Dynamic Learning Rate for Test-time Adaptation on Cross-domain Medical Images. Hongzheng Yang, Cheng Chen^#, Meirui Jiang, Quande Liu, Jianfeng Cao, Pheng Ann Heng, Qi Dou. IEEE Transactions on Medical Imaging (TMI), 2022 [paper][code] (# indicates corresponding authorship)
	Single-domain Generalization in Medical Image Segmentation via Test-time Adaptation from Shape Dictionary. Quande Liu, Cheng Chen, Qi Dou, Pheng Ann Heng. AAAI Conference on Artificial Intelligence (AAAI), 2022. [paper][code]
	Learning with Privileged Multimodal Knowledge for Unimodal Segmentation. Cheng Chen, Qi Dou, Yueming Jin, Quande Liu, Pheng Ann Heng. IEEE Transactions on Medical Imaging (TMI), 2021. [paper][code]
	Source-Free Domain Adaptive Fundus Image Segmentation with Denoised Pseudo-Labeling. Cheng Chen, Quande Liu, Yueming Jin, Qi Dou, Pheng Ann Heng. Medical Image Computing and Computer Assisted Interventions (MICCAI), 2021. [paper][code]
	FedDG: Federated Domain Generalization on Medical Image Segmentation via Episodic Learning in Continuous Frequency Space. Quande Liu, Cheng Chen, Jing Qin, Qi Dou, Pheng-Ann Heng IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021. [paper][code]
	Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation. Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng. IEEE Transactions on Medical Imaging (TMI), 2020. (ESI Highly Cited Paper) [paper][code]
	Robust Multimodal Brain Tumor Segmentation via Feature Disentanglement and Gated Fusion. Cheng Chen, Qi Dou, Yueming Jin, Hao Chen, Jing Qin, Pheng-Ann Heng. Medical Image Computing and Computer Assisted Intervention (MICCAI), 2019. (Graduate Student Travel Award) [paper][code]
	Synergistic Image and Feature Adaptation: Towards Cross-Modality Domain Adaptation for Medical Image Segmentation. Cheng Chen, Qi Dou, Hao Chen, Jing Qin, Pheng-Ann Heng. Association for the Advancement of Artificial Intelligence (AAAI), 2019. (Oral) (Graduate Student Scholarship) [paper][code]
	Semantic-aware Generative Adversarial Nets for Unsupervised Domain Adaptation in Chest X-ray Segmentation. Cheng Chen, Qi Dou, Hao Chen, Pheng-Ann Heng. International Workshop on Machine Learning on Medical Imaging (MLMI), 2018. (Oral) [paper]
	Unsupervised Cross-modality Domain Adaptation of Convnets for Biomedical Image Segmentations with Adversarial Loss. Qi Dou, Cheng Ouyang, Cheng Chen, Hao Chen, Pheng-Ann Heng. International Joint Conference on Artificial Intelligence (IJCAI), 2018. (Oral) [paper][code]

Patent

Apparatus and method for motion tracking of at least a portion of a limb.
Cheng Chen, Jim Wang.
WO Patent WO2017005591A1, 2017.

Honors & Awards

World’s Top 2% Scientists by Stanford University, 2024

MICCAI'19 Graduate Student Travel Award, 2019

AAAI'19 Student Scholarship, 2019

Finalist EMBC Student Paper Competition, 2013

The Johns Hopkins University BME-MSE Full Scholarship, 2011-2013

Outstanding Graduates of Hangzhou, 2011

Outstanding Undergraduate Thesis of Zhejiang University, 2011

Professional Activities

Conference/Journal Reviews:
International Conference on Medical Image Computing and Computer-Assisted Intervention (MICCAI) 2021
Medical Imaging with Deep Learning (MIDL) 2021
IEEE Conference on Computer Vision and Pattern Recognition (CVPR) 2021
Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI) 2021
Medical Image Analysis (MedIA)
IEEE Transactions on Medical Imaging (TMI)
International Journal of Computer Vision (IJCV)
Journal of Biomedical and Health Informatics (JBHI)
IEEE Transactions on Geoscience and Remote Sensing (TGRS)
IEEE Access
Mathematical Problems in Engineering

Teaching

2017-2018	Spring	Linear Algebra and Vector Calculus for Engineers
2017-2018	Fall	Digital Logic And Systems