Yichi Zhang

weixs 

Ph.D Candidate, Fudan University. Email: zhangyichi23@m.fudan.edu.cn

Github | Google Scholar | Semantic Scholar | ResearchGate

  • I am currently a Ph.D. candidate at School of Data Science and Artificial Intelligence Innovation and Incubation Institute, Fudan University, co-advised by Prof. Yuan Qi and Prof. Yuan Cheng. Before that, I obtained Bachelor's degree and Master's degree from Beihang University in 2020 and 2023.
  • My research interests lie in the interdisciplinary field of artificial intelligence and healthcare. I have published 20+ peer-reviewed journal/conference articles. Currently, my research focus on 1) Developing vision/language foundation models applicable for efficient medical image analysis, with a particular focus on molecular imaging applications like PET image analysis. 2) Deploying AI techniques in real-world biomedicine and healthcare scenarios.
  • Recent News

    [2026.3] πŸ’‘ We release an updated version of SegAnyPET with thorough assessment on multi-center, multi-tracer, multi-disease datasets and evaluation of clinical utility in downstream applications.
    [2026.3] πŸŽ‰ Our work PETWB-REP was accepted by Scientific Data. The dataset is publicly available.
    [2026.2] πŸŽ‰ Our work TAR on topological-aware semi-supervised segmentation was accepted by MIDL 2026, congrats to Feiyang.
    [2026.1] 🏒 Attending AAAI 2026. Looking forward to seeing you in Singapore πŸ‡ΈπŸ‡¬.
    [2025.11] πŸŽ‰ Our work PET2Rep on whole-body PET/CT radiology report generation benchmark was accepted by AAAI 2026, thanks to all co-authors.
    [2025.10] 🏒 Attending ICCV 2025. Looking forward to seeing you in Holonunu, Hawai'i.
    [2025.10] πŸŽ‰ Two papers were accepted by Radiotherapy and Oncology and Biomedical Signal Processing and Control.
    [2025.9] πŸŽ™οΈ Invited to give a talk at Shanghai Foundation Model Innovation Center (ζ¨‘ι€Ÿη©Ίι—΄).
    [2025.9] ✨ Our project SAM4MIS received the 1000th star, a milestone moment for us!
    [2025.8] πŸ’‘ We release PET2Rep, the first whole-body PET/CT image-report dataset for benchmarking VLMs for radiology report generation.
    [2025.8] ✨ Some of my works are ranked among the most-cited papers (2020-2025) by Google Scholar Metrics [1], [2].
    [2025.7] πŸŽ‰ Our work SemiSAM+ on foundation model-driven semi-supervised medical image segmentation was accepted by Medical Image Analysis, thanks to all co-authors.
    [2025.6] πŸŽ‰ Our work SegAnyPET on foundation model for universal PET segmentation was accepted by ICCV 2025, thanks to all co-authors.

    Selected Publications

    Foundation Models for Medical Imaging

     
     
     
     
     
     
     
     
     
     
     

    Data/Label-Efficient Learning for Medical Image Analysis

     
     
     
     
     
     

    Dataset/Benchmark for Biomedical Imaging and Healthcare Applications

     
     
     
     
     
    • Deep learning methods for automatic evaluation of delayed enhancement-MRI. The results of the EMIDEC challenge
      Alain Lalande, Zhihao Chen, Thibaut Pommier, Thomas Decourselle, Abdul Qayyum, Michel Salomon, Dominique Ginhac, Youssef Skandarani, Arnaud Boucher, Khawla Brahim, Marleen de Bruijne, Robin Camarasa, Teresa M. Correia, Xue Feng, Kibrom B. Girum, Anja Hennemuth, Markus Huellebrand, Raabid Hussain, Matthias Ivantsits, Jun Ma, Craig Meyer, Rishabh Sharma, Jixi Shi, Nikolaos V. Tsekos, Marta Varela, Xiyue Wang, Sen Yang, Hannu Zhang, Yichi Zhang, Yuncheng Zhou, Xiahai Zhuang, Raphael Couturier, Fabrice Meriaudeau.
      Medical Image Analysis, 2022. (SCI Q1 TOP, IF=13.828)

     
    • AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?
      Jun Ma, Yao Zhang, Song Gu, Cheng Zhu, Cheng Ge, Yichi Zhang, Xingle An, Congcong Wang, Qiyuan Wang, Xin Liu, Shucheng Cao, Qi Zhang, Shangqing Liu, Yunpeng Wang, Yuhui Li, Jian He, Xiaoping Yang.
      IEEE Transactions on Pattern Analysis and Machine Intelligence, 2021. (SCI Q1 TOP, IF=17.861) [code]
      * ESI Highly Cited Paper

    Model-Centric Advancements for Medical Image Analysis

     
     
     
     
     

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