Swarm learning with weak supervision enables automatic breast cancer detection in magnetic resonance imaging.

ABSTRACT:

Background

Over the next 5 years, new breast cancer screening guidelines recommending magnetic resonance imaging (MRI) for certain patients will significantly increase the volume of imaging data to be analyzed. While this increase poses challenges for radiologists, artificial intelligence (AI) offers potential solutions to manage this workload. However, the development of AI models is often hindered by manual annotation requirements and strict data-sharing regulations between institutions.

Methods

In this study, we present an integrated pipeline combining weakly supervised learning-reducing the need for detailed annotations-with local AI model training via swarm learning (SL), which circumvents centralized data sharing. We utilized three datasets comprising 1372 female bilat

SUBMITTER: Saldanha OL 

PROVIDER: S-EPMC11802753 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature