Medical image classification plays a crucial role in computer-aided clinical diagnosis. While deep learning techniques have significantly enhanced efficiency and reduced costs, the privacy-sensitive nature of medical imaging data complicates centralized storage and model training. Furthermore, low-resource healthcare organizations face challenges related to communication overhead and efficiency due to increasing data and model scales. This paper proposes a novel privacy-preserving medical image classification framework based on federated learning to address these issues, named FedMIC. The framework enables healthcare organizations to learn from both global and local knowledge, enhancing local representation of private data despite statistical heterogeneity. It provides customized models for organizations with diverse data distributions while minimizing communication overhead and improving efficiency without compromising performance. Our FedMIC enhances robustness and practical applicability under resource-constrained conditions. We demonstrate FedMIC's effectiveness using four public medical image datasets for classical medical image classification tasks.

本论文提出了一种基于联邦学习的隐私保护医学图像分类框架(FedMIC)，通过全局和局部知识使医疗组织从私有数据中获得定制模型，同时减少通信开销和提高效率，提高在资源受限条件下的稳健性和实际适用性。通过使用四个公共医学图像数据集展示了FedMIC的有效性。

医学图像分类的联邦蒸馏：走向可靠的计算机辅助诊断