7 papers
FedDOSE: Federated Learning Framework Decomposing Site Effects for Modeling Brain Dynamic Functional Connectivity
Deepank Girish, Yi Hao Chan, Yubin Zheng +2
Functional Magnetic Resonance Imaging ( fMRI ) data are often pooled into collaborative multi-site consortia, as deep learning models for analyses require large datasets to general…
ReMAP-PET: Beyond Visual Understanding -- Learning Region-Guided Metabolic Alignment Semantics from Brain PET
Dasen Dai, Yanteng Zhang, Shuoqi Li +6
Positron Emission Tomography (PET) reveals brain metabolism and is clinically central to neurodegenerative disease assessment, yet existing 3D brain foundation models treat PET as…
Structural Interpretations of Protein Language Model Representations via Differentiable Graph Partitioning
Siddhant Dutta, Edward Tan Beng Wai, Soumick Sarker +2
Protein language models such as ESM-2 learn rich residue representations that achieve strong performance on protein function prediction, but their features remain difficult to inte…
Foundation models for discovering robust biomarkers of neurological disorders from dynamic functional connectivity
Deepank Girish, Yi Hao Chan, Sukrit Gupta +2
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable mod…
FedDEAP: Adaptive Dual-Prompt Tuning for Multi-Domain Federated Learning
Yubin Zheng, Pak-Hei Yeung, Jing Xia +4
Federated learning (FL) enables multiple clients to collaboratively train machine learning models without exposing local data, balancing performance and privacy. However, domain sh…
Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model
Pak-Hei Yeung, Jayroop Ramesh, Pengfei Lyu +2
This paper explores the transfer of knowledge from general vision models pretrained on 2D natural images to improve 3D medical image segmentation. We focus on the semi-supervised s…