Scholarlink

AI systems must be able to make use of large, diverse, and global information in order to build robust and efficient systems in the medical imaging field. To develop a global model, all these facts must be collected in one place, but this raises questions about privacy and ownership. In this study, we evaluated multiple federated learning methods for segmenting brain tumors. Federated learning utilizes all accessible data without storing or disclosing collaborators’ personal information on a central server. By appropriately integrating these model updates, a high degree of accuracy could be attained, but doing so increases the possibility that the shared model might accidentally leak the local training data. We have selected the best model from U-Net and DeepLabV3 to obtain the best efficiency.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Segmenting MRI Images Using Federated Learning for Brain Tumor Detection

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • T. L. Akshaya,
  • K. Swetha,
  • S. Pravalika,
  • U. Chandrasekhar

摘要

AI systems must be able to make use of large, diverse, and global information in order to build robust and efficient systems in the medical imaging field. To develop a global model, all these facts must be collected in one place, but this raises questions about privacy and ownership. In this study, we evaluated multiple federated learning methods for segmenting brain tumors. Federated learning utilizes all accessible data without storing or disclosing collaborators’ personal information on a central server. By appropriately integrating these model updates, a high degree of accuracy could be attained, but doing so increases the possibility that the shared model might accidentally leak the local training data. We have selected the best model from U-Net and DeepLabV3 to obtain the best efficiency.