Chenchu Xu
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Chenchu Xu [En/中文] [Google Scholar] [Anhui University]
Professor |
I am a professor at School of Computer Science and Technology, Anhui University (AHU). Before joining AHU, I was a postdoc at Schulich School of Medicine & Dentistry, Western University (UWO), Canada, working with Prof. Shuo Li. I obtained the Ph.D in computer Science from Anhui University and the M.Sc. degree in computer science from the Hefei University of Technology and the B.Sc. degree in network engineering from the University of Electronic Engineering
I am always looking for highly-motivated students with strong mathematical background or excellent coding skills. If you are interested in working with me, please send me an email about your interests and background (attaching your CV, transcripts, and any previous research papers).
Research Interests
Lie in the areas of Computer Vision, Machine Learning, and Medical Image Analysis. To enable artificial intelligence to change the practice of medicine; Seek to enable artificial intelligence to move out of the lab and into real clinic settings.
News
02/2026, Our research, 'Diversity-driven MG-MAE: Multi-granularity representation learning for non-salient object segmentation,' has been published in Medical Image Analysis (2026)!
10/2025, Our research, 'Knowledge-driven interpretative conditional diffusion model for contrast-free myocardial infarction enhancement synthesis,' has been published in Medical Image Analysis (2025)!
09/2025, Our research, 'Interactive prototype learning and self-learning for few-shot medical image segmentation,' has been published in Artificial Intelligence in Medicine (2025)!
09/2025, Our research, 'Non-Salient Object Segmentation in Medical Images via Pre-trained Multi-Granularity Masked Autoencoders,' has been published in MICCAI 2025!
01/2025, Our research, 'Hierarchical candidate recursive network for highlight restoration in endoscopic videos,' has been published in Expert Systems with Applications! As pioneers, we have ventured into the entirely new field of highlight restoration in endoscopic videos, laying the foundation for the development of this technology.
06/2024, Our research, 'Accurate segmentation of liver tumor from multi-modality non-contrast images using a dual-stream multi- level fusion framework,' has been published in Computerized Medical Imaging and Graphics!
06/2024, Our research, 'Prediction of Freezing of Gait in Parkinson’s disease based on multi-channel time-series neural network,' has been published in Artificial Intelligence in Medicine!
05/2023, Thrilled to announce that master student Ronghui Qi’s paper, 'Cardiac Physiology Knowledge-driven Diffusion Model for Contrast-free Synthesis Myocardial Infarction Enhancement', has secured an acceptance by MICCAI 2024!
05/2024, Our research, 'Common-Unique Decomposition Driven Diffusion Model for Contrast-Enhanced Liver MR Images Multi-Phase Interconversion,' has been published in IEEE Journal of Biomedical and Health Informatics!
04/2024, Our research, 'Deep Generative Adversarial Reinforcement Learning for Semi-Supervised Segmentation of Low-Contrast and Small Objects in Medical Images,' has been published in IEEE Transactions on Medical Imaging!
10/2023, Our research was distinguished with notable recognition, being featured as the Runner-Up for the Best Paper Award in the Elsevier-MedIA MICCAI 2022 Special Issue. [Click for details]
10/2023, Our master's students, Jinhao Liu and Xinglai Zhu, as first and second authors, have published their innovative research, 'Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework,' in Applied Soft Computing！
09/2023, We're delighted to announce our paper, 'Spatiotemporal Knowledge Teacher-Student Reinforcement Learning to Detect Liver Tumors Without Contrast Agents,' has been accepted by Medical Image Analysis, heralding a pivotal advance in non-invasive liver tumor detection methods!
07/2023, Thrilled to share that as the corresponding author, our latest work 'Heuristic multi-modal integration framework for liver tumor detection from multi-modal non-enhanced MRIs' has been published in Expert Systems with Applications!