Robust Learning Protocol for Federated Tumor Segmentation Challenge

2022-12-16 · Ambrish Rawat, Giulio Zizzo, Swanand Kadhe, Jonathan P. Epperlein, Stefano Braghin

In this work, we devise robust and efficient learning protocols for orchestrating a Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2022). Enabling FL for FeTS setup is challenging mainly due to data heterogeneity among collaborators and communication cost of training. To tackle these challenges, we propose Robust Learning Protocol (RoLePRO) which is a combination of server-side adaptive optimisation (e.g., server-side Adam) and judicious parameter (weights) aggregation schemes (e.g., adaptive weighted aggregation). RoLePRO takes a two-phase approach, where the first phase consists of vanilla Federated Averaging, while the second phase consists of a judicious aggregation scheme that uses a sophisticated reweighting, all in the presence of an adaptive optimisation algorithm at the server. We draw insights from extensive experimentation to tune learning rates for the two phases.

📄 PDF Abstract BibTeX arXiv:2212.08290

Code (0)

등록된 구현이 없습니다.

Tasks

Federated Learning Tumor Segmentation

Recommender Engine Driven Client Selection in Federated Brain Tumor Segmentation

2024-12-28 · Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi

This study presents a robust and efficient client selection protocol designed to optimize the Federated Learning (FL) process for the Federated Tumor Segmentation Challenge (FeTS 2024). In the evolving landscape of FL, t…

Brain Tumor Segmentation Collaborative Filtering Federated Learning Segmentation+1

Federated Multi-Task Learning for Bladder Tumor Segmentation and MIBC Classification Using a Hybrid CNN-Transformer Architecture

2026-08-31 · Malhar Udmale, Divyanshu Dwivedi, Aarohi Dhand, Sachin Dudda Nagaraju 외 arxiv

Accurate bladder tumor segmentation and assessment of mus- cle invasion from T2-weighted MRI are important for treatment plan- ning, but developing robust models across institutions is challenging be- cause patient data …

Multi-Task Learning Tumor Segmentation

The MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024: Efficient and Robust Aggregation Methods for Federated Learning

2025-12-05 · Akis Linardos, Sarthak Pati, Ujjwal Baid, Brandon Edwards 외 arxiv

We present the design and results of the MICCAI Federated Tumor Segmentation (FeTS) Challenge 2024, which focuses on federated learning (FL) for glioma sub-region segmentation in multi-parametric MRI and evaluates new we…

Federated Learning Tumor Segmentation

Election of Collaborators via Reinforcement Learning for Federated Brain Tumor Segmentation

2024-12-28 · Muhammad Irfan Khan, Elina Kontio, Suleiman A. Khan, Mojtaba Jafaritadi

Federated learning (FL) enables collaborative model training across decentralized datasets while preserving data privacy. However, optimally selecting participating collaborators in dynamic FL environments remains challe…

Brain Tumor Segmentation Federated Learning Lesion Segmentation Reinforcement Learning (RL)+2

The Federated Tumor Segmentation (FeTS) Challenge

2021-05-12 · Sarthak Pati, Ujjwal Baid, Maximilian Zenk, Brandon Edwards 외

This manuscript describes the first challenge on Federated Learning, namely the Federated Tumor Segmentation (FeTS) challenge 2021. International challenges have become the standard for validation of biomedical image ana…

Brain Tumor Segmentation Federated Learning Segmentation Tumor Segmentation