Federated Learning for Decentralized Artificial Intelligence in Melanoma Diagnostics.

ABSTRACT:

Importance

The development of artificial intelligence (AI)-based melanoma classifiers typically calls for large, centralized datasets, requiring hospitals to give away their patient data, which raises serious privacy concerns. To address this concern, decentralized federated learning has been proposed, where classifier development is distributed across hospitals.

Objective

To investigate whether a more privacy-preserving federated learning approach can achieve comparable diagnostic performance to a classical centralized (ie, single-model) and ensemble learning approach for AI-based melanoma diagnostics.

Design, setting, and participants

This multicentric, single-arm diagnostic study developed a federated model for melanoma-nevus classification using histopathological

SUBMITTER: Haggenmuller S 

PROVIDER: S-EPMC10851139 | biostudies-literature | 2024 Mar

REPOSITORIES: biostudies-literature