Multiple Sclerosis
Multiple sclerosis (MS) is a chronic neurological disease characterized by lesions in the brain and spinal cord, and research focuses on improving diagnosis, monitoring disease progression, and predicting treatment response. Current research employs advanced machine learning techniques, including convolutional neural networks (CNNs), recurrent neural networks (RNNs like LSTMs), graph neural networks (GNNs), and generative adversarial networks (GANs), often incorporating multimodal data (MRI, EHR) and leveraging federated learning to address data scarcity and privacy concerns. These efforts aim to develop more accurate and efficient diagnostic tools, personalized treatment strategies, and a better understanding of disease mechanisms, ultimately improving patient outcomes and clinical decision-making.
Papers
Self-pruning Graph Neural Network for Predicting Inflammatory Disease Activity in Multiple Sclerosis from Brain MR Images
Chinmay Prabhakar, Hongwei Bran Li, Johannes C. Paetzold, Timo Loehr, Chen Niu, Mark Mühlau, Daniel Rueckert, Benedikt Wiestler, Bjoern Menze
Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training
Lei Bai, Dongang Wang, Michael Barnett, Mariano Cabezas, Weidong Cai, Fernando Calamante, Kain Kyle, Dongnan Liu, Linda Ly, Aria Nguyen, Chun-Chien Shieh, Ryan Sullivan, Hengrui Wang, Geng Zhan, Wanli Ouyang, Chenyu Wang