3 papers
cs.CV2025
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…
eess.IV2025
Bridging the Inter-Domain Gap through Low-Level Features for Cross-Modal Medical Image Segmentation
Pengfei Lyu, Pak-Hei Yeung, Xiaosheng Yu +4
This paper addresses the task of cross-modal medical image segmentation by exploring unsupervised domain adaptation (UDA) approaches. We propose a model-agnostic UDA framework, Low…
cs.LG2025
Discovering robust biomarkers of psychiatric disorders from resting-state functional MRI via graph neural networks: A systematic review
Yi Hao Chan, Deepank Girish, Sukrit Gupta +5
Graph neural networks (GNN) have emerged as a popular tool for modelling functional magnetic resonance imaging (fMRI) datasets. Many recent studies have reported significant improv…