Federated Learning Models Show Potential in Melanoma-Nevus Classification, but Improvements Needed
Source: AJMC, March 2024
A multicentric, single-arm diagnostic study created a decentralized federated learning model for the classification of invasive melanomas and nevi, showcasing comparable results to centralized data models.
A federated learning (FL) model demonstrated great promise in the binary classification of nevi and invasive melanomas while showcasing the benefits that artificial intelligence (AI) can provide regarding privacy, global collaboration, and image classification in melanoma diagnostics, according to a recent study published in JAMA Dermatology.
The earlier that melanoma can be detected, the better outcomes a patient typically experiences; however, this detection isn’t without its challenges. Atypical nevi carry a degree of morphological overlap with melanoma that can affect the cancer’s identification. Prior research, the present authors add, has demonstrated the potential use—if not superiority—of convolutional neural networks (artificial programs engineered for image/pattern recognition) to successfully perform histopathological and dermatological aims compared with human specialists.