How Much Data are Enough? Investigating Dataset Requirements for Patch-Based Brain MRI Segmentation Tasks

no code implementations • 4 Apr 2024 • Dongang Wang, Peilin Liu, Hengrui Wang, Heidi Beadnall, Kain Kyle, Linda Ly, Mariano Cabezas, Geng Zhan, Ryan Sullivan, Weidong Cai, Wanli Ouyang, Fernando Calamante, Michael Barnett, Chenyu Wang

This paper focuses on an early stage phase of deep learning research, prior to model development, and proposes a strategic framework for estimating the amount of annotated data required to train patch-based segmentation networks.

MRI segmentation

Improving Multiple Sclerosis Lesion Segmentation Across Clinical Sites: A Federated Learning Approach with Noise-Resilient Training

no code implementations • 31 Aug 2023 • 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

Our approach enables collaboration among multiple clinical sites without compromising data privacy under a federated learning paradigm that incorporates a noise-robust training strategy based on label correction.

Federated Learning Lesion Segmentation

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

no code implementations • 3 May 2022 • Dongnan Liu, Mariano Cabezas, Dongang Wang, Zihao Tang, Lei Bai, Geng Zhan, Yuling Luo, Kain Kyle, Linda Ly, James Yu, Chun-Chien Shieh, Aria Nguyen, Ettikan Kandasamy Karuppiah, Ryan Sullivan, Fernando Calamante, Michael Barnett, Wanli Ouyang, Weidong Cai, Chenyu Wang

In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training.

Federated Learning Lesion Segmentation +1