FedDermaSeg: Federated Learning for Dermatological Image Segmentation

Key points

• Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment planning.1
• Automated skin lesion analysis can assist dermatologists, with lesion segmentation serving as a fundamental step in computer-aided diagnostic systems.1
• Conventional deep learning-based segmentation models typically rely on centralized training, where images and their corresponding segmentation masks are collected on a central server.1
• The training and validation sets of the ISIC 2018 Skin Lesion Segmentation Challenge dataset are used to simulate a distributed learning environment and develop a federated segmentation model.1

Sources (1)

• [1]FedDermaSeg: Federated Learning for Dermatological Image Segmentation
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 03:49 PM

  To address these limitations, we investigate the feasibility of federated learning for privacy-preserving skin lesion segmentation.

  Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment planning.

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