Multi-task Federated Learning for Heterogeneous Pancreas Segmentation

08/19/2021
by   Chen Shen, et al.
0

Federated learning (FL) for medical image segmentation becomes more challenging in multi-task settings where clients might have different categories of labels represented in their data. For example, one client might have patient data with "healthy” pancreases only while datasets from other clients may contain cases with pancreatic tumors. The vanilla federated averaging algorithm makes it possible to obtain more generalizable deep learning-based segmentation models representing the training data from multiple institutions without centralizing datasets. However, it might be sub-optimal for the aforementioned multi-task scenarios. In this paper, we investigate heterogeneous optimization methods that show improvements for the automated segmentation of pancreas and pancreatic tumors in abdominal CT images with FL settings.

READ FULL TEXT
research
03/25/2022

ST-FL: Style Transfer Preprocessing in Federated Learning for COVID-19 Segmentation

Chest Computational Tomography (CT) scans present low cost, speed and ob...
research
04/28/2023

Quality-Adaptive Split-Federated Learning for Segmenting Medical Images with Inaccurate Annotations

SplitFed Learning, a combination of Federated and Split Learning (FL and...
research
03/12/2022

Auto-FedRL: Federated Hyperparameter Optimization for Multi-institutional Medical Image Segmentation

Federated learning (FL) is a distributed machine learning technique that...
research
04/20/2022

Federated Learning in Multi-Center Critical Care Research: A Systematic Case Study using the eICU Database

Federated learning (FL) has been proposed as a method to train a model o...
research
04/05/2022

Federated Cross Learning for Medical Image Segmentation

Federated learning (FL) can collaboratively train deep learning models u...
research
05/04/2022

FedMix: Mixed Supervised Federated Learning for Medical Image Segmentation

The purpose of federated learning is to enable multiple clients to joint...
research
07/14/2022

Accelerated Federated Learning with Decoupled Adaptive Optimization

The federated learning (FL) framework enables edge clients to collaborat...

Please sign up or login with your details

Forgot password? Click here to reset