The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions.

ABSTRACT: Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes. High-performing MOD models necessitate large datasets accurately reflecting the feature distribution of the corresponding problem domain. However, strict privacy regulations protecting patient data often hinder data consolidation, negatively affecting the performance and generalization of MOD models. Federated Learning (FL) offers a solution by enabling model training while the data remain at its original source institution. While existing FL solutions for medical image classification and segmentation demonstrate promising performance, FL for MOD remains largely unexplored. Motivated by this lack of technical so

SUBMITTER: Rashidi G 

PROVIDER: S-EPMC11470020 | biostudies-literature | 2024 Oct

REPOSITORIES: biostudies-literature