News
[IEEE-TIP] Rethinking Self-training for Semi-supervised Landmark Detection: A Selection-free Approach
A new study on computational pathology from HKUST Smart Lab on semi-supervised landmark detection has been accepted by IEEE Transactions on Image Processing.
Last updated on 2024/09/16
[IEEE-TMI] Shapley Values-enabled Progressive Pseudo Bag Augmentation for Whole Slide Image Classification
A new study on computational pathology from HKUST Smart Lab on progressive pseudo bag augmentation based on Shapley values has been accepted by IEEE Transactions on Medical Imaging.
Last updated on 2024/09/14
[IEEE-TMI] Cohort-Individual Cooperative Learning for Multimodal Cancer Survival Analysis
A new study from the HKUST Smart Lab on multimodal medical data fusion related to cancer survival analysis has been accepted by IEEE Transactions on Medical Imaging.
Last updated on 2024/09/12
[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
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