홈 › Papers

MS Lesion Segmentation: Revisiting Weighting Mechanisms for Federated Learning

2022-05-03 · 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

Federated learning (FL) has been widely employed for medical image analysis to facilitate multi-client collaborative learning without sharing raw data. Despite great success, FL's performance is limited for multiple sclerosis (MS) lesion segmentation tasks, due to variance in lesion characteristics imparted by different scanners and acquisition parameters. In this work, we propose the first FL MS lesion segmentation framework via two effective re-weighting mechanisms. Specifically, a learnable weight is assigned to each local node during the aggregation process, based on its segmentation performance. In addition, the segmentation loss function in each client is also re-weighted according to the lesion volume for the data during training. Comparison experiments on two FL MS segmentation scenarios using public and clinical datasets have demonstrated the effectiveness of the proposed method by outperforming other FL methods significantly. Furthermore, the segmentation performance of FL incorporating our proposed aggregation mechanism can exceed centralised training with all the raw data. The extensive evaluation also indicated the superiority of our method when estimating brain volume differences estimation after lesion inpainting.

📄 PDF Abstract BibTeX arXiv:2205.01509

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning Lesion SegmentationMedical Image AnalysisSegmentation

Universal Loss Reweighting to Balance Lesion Size Inequality in 3D Medical Image Segmentation

2020-07-20 · Boris Shirokikh, Alexey Shevtsov, Anvar Kurmukov, Alexandra Dalechina 외

Target imbalance affects the performance of recent deep learning methods in many medical image segmentation tasks. It is a twofold problem: class imbalance - positive class (lesion) size compared to negative class (non-l…

Image Segmentation Medical Image Segmentation Semantic Segmentation

Longitudinal Segmentation of MS Lesions via Temporal Difference Weighting

2024-09-20 · Maximilian Rokuss, Yannick Kirchhoff, Saikat Roy, Balint Kovacs 외

Accurate segmentation of Multiple Sclerosis (MS) lesions in longitudinal MRI scans is crucial for monitoring disease progression and treatment efficacy. Although changes across time are taken into account when assessing …

Inductive Bias Lesion Detection Lesion Segmentation

Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting

2021-07-27 · Brennan Nichyporuk, Justin Szeto, Douglas L. Arnold, Tal Arbel

There are many clinical contexts which require accurate detection and segmentation of all focal pathologies (e.g. lesions, tumours) in patient images. In cases where there are a mix of small and large lesions, standard b…

Lesion Detection Segmentation

ADP-FL-MedSeg: Adaptive Differential Privacy for Federated Medical Segmentation Across Diverse Modalities

2026-04-07 · Puja Saha, Eranga Ukwatta arxiv

Large volumes of medical data remain underutilized because centralizing distributed data is often infeasible due to strict privacy regulations and institutional constraints. In addition, models trained in centralized set…

Medical Image Segmentation Brain Tumor Segmentation Skin Lesion Segmentation Federated Learning

Quality monitoring of federated Covid-19 lesion segmentation

2021-12-16 · Camila Gonzalez, Christian Harder, Amin Ranem, Ricarda Fischbach 외

Federated Learning is the most promising way to train robust Deep Learning models for the segmentation of Covid-19-related findings in chest CTs. By learning in a decentralized fashion, heterogeneous data can be leverage…

Federated Learning Lesion Segmentation Segmentation