Huazhu FU

Principal Scientist
Institute of High Performance Computing (IHPC)
Agency for Science, Technology and Research (A*STAR), Singapore.

Email: hzfu(AT)ieee(DOT)org

[Recent News] - [Professional Activities] - [Highlighted Publications] - [Recognitions & Awards] - [Google Scholar]
I am a principal scientist at IHPC, A*STAR. I received my Ph.D. from Tianjin University in 2013. Previously, I were a Research Fellow (2013-2015) at Nanyang Technological University (NTU), Singapore, a Research Scientist (2015-2018) at Institute for Infocomm Research (I2R), A*STAR, Singapore, and a Senior Scientist (2018-2021) at Inception Institute of Artificial Intelligence (IIAI), UAE.
Major focuses on:
  • Computer Vision: Foreground Detection, Image Segmentation, Image Restoration.
  • AI for Healthcare: Medical Image Analysis, ⚡Medical Vision-Language Model, ⚡Medical Foundation Model.
  • Trustworthy AI: ⚡Uncertainty Estimation, ⚡Federated Learning.

  • Recent News:

    • [11/2024] Happy to be recognized as "Highly Cited Researcher" by Web of Science (in the field of Cross-Field), Clarivate.
    • [10/2024] One paper accepted by Cell Reports Medicine: "Enhancing Al Reliability: A Foundation Model with Uncertainty Estimation for Optical Coherence Tomography based Retinal Diseases Diagnosis".
    • [10/2024] One paper accepted by IEEE TMI: "CoD-MIL: Chain-of-Diagnosis Prompting Multiple Instance Learning for Whole Slide Image Classification".
    • [20/2024] Happy to receive the Best Paper Award of Ophthalmic Medical Image Analysis (OMIA) Workshop in MICCAI, 2024.
    • [10/2024] One paper accepted by npj Digital Medicine: "Early Detection of Dementia through Retinal Imaging and Trustworthy AI".
    • [09/2024] Two papers accepted by NeurIPS 2024.
    • [09/2024] One paper accepted by EMNLP 2024: "Self-Training Large Language and Vision Assistant for Medical".
    • [08/2024] One paper accepted by IEEE TPAMI: "Say No to Freeloader: Protecting Intellectual Property of Your Deep Model".
    • [08/2024] One paper accepted by MedIA: "E2-MIL: An Explainable and Evidential Multiple Instance Learning Framework for Whole Slide Image Classification".
    • [07/2024] One paper accepted by IEEE TPAMI: "Structure Unbiased Adversarial Model for Medical Image Translation".
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    Professional Activities:

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    Highlighted Publications:

    More publication could be found in [Google Scholar]
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    Recognitions & Awards:

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