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.

翻译：医学图像分类在计算机辅助临床诊断中扮演着关键角色。尽管深度学习技术显著提升了效率并降低了成本，但医学影像数据的隐私敏感性使得集中式存储与模型训练变得复杂。此外，随着数据和模型规模的不断增长，资源有限的医疗机构在通信开销和效率方面面临挑战。本文提出了一种基于联邦学习的新型隐私保护医学图像分类框架以解决上述问题，命名为FedMIC。该框架使医疗机构能够同时学习全局与局部知识，在统计异质性条件下增强对私有数据的局部表征能力。它为具有不同数据分布的机构提供定制化模型，同时在不牺牲性能的前提下最小化通信开销并提升效率。我们的FedMIC在资源受限条件下增强了鲁棒性与实际适用性。我们使用四个公共医学图像数据集在经典医学图像分类任务上验证了FedMIC的有效性。