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Non-Invasive and Personalized Management of Breast Cancer Patients through a Large Mixture of Modality Experts Model for Multiparametric MRI
This collaborative research project with multiple institutions collects the world's largest multiparametric breast MRI dataset to develop a Large Mixture of Modality Experts model (MOME) for non-invasive personalized breast cancer diagnosis, grading, and treatment prediction.
Last updated on 2024/08/28
Towards A Generalizable Pathology Foundation Model via Unified Knowledge Distillation
See our latest work on pathology foundation model GPFM, which distills knowledge from multiple expert models and achieves the best average rank of 1.36 across 39 diverse clinical tasks.
Last updated on 2024/08/01
A Multimodal Knowledge-Enhanced Whole-Slide Pathology Foundation Model: A New Pathology Pretraining Paradigm
See our latest work on pathology foundation model mSTAR, which leverages multimodal knowledge to enhance these models while achieving whole-slide-level pretraining.
Last updated on 2024/07/31
[MICCAI 2024 Paper] Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective
Our paper titled 'Rethinking Autoencoders for Medical Anomaly Detection from A Theoretical Perspective' has been accepted by MICCAI 2024.
Last updated on 2024/07/27
[MICCAI 2024 Paper & HKUST CSE Best FYP] Aligning Medical Image Analysis Tasks with General Knowledge from Large Language Models
A study on medical vision-language models by HKUST Smart Lab has been accepted for publication at MICCAI 2024. This paper has been awarded the 2024 Best Final Year Project (CSE Best FYP Award) by the Department of Computer Science and Engineering at Hong Kong University of Science and Technology. Congratulations to the authors!
Last updated on 2024/07/26