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[Nature Communications] Learning Co-plane Attention across MRI Sequences for Diagnosing Twelve Types of Knee Abnormalities

The Smart Lab team at the Hong Kong University of Science and Technology collaborated with The Third Affiliated Hospital of Southern Medical University and proposed a deep learning method that incorporates Co-Plane Attention across MRI Sequences (CoPAS) to classify knee abnormalities. The model outperforms junior radiologists and remains competitive with senior radiologists. With the assistance of model output, the diagnosis accuracy of all radiologists was improved significantly.

Last updated on 2024/09/08

[Nature Communications] 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