发表机构
Indian Institute of Science Education and Research Berhampur; Indian Institute of Technology Jodhpur（印度科学教育与研究学院贝兰普尔校区; 印度理工学院焦特布尔校区）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对皮肤病变分割中集中式训练带来的隐私和资源问题，提出基于联邦学习的方法，在ISIC 2018和PH2数据集上验证其性能与集中式相当且优于本地模型。
AI 中文摘要
皮肤癌是一个重大的全球健康问题，早期检测和准确的病变勾画对于有效的诊断和治疗规划至关重要。自动化皮肤病变分析可以辅助皮肤科医生，其中病变分割是计算机辅助诊断系统中的基础步骤。传统的基于深度学习的分割模型通常依赖于集中式训练，即图像及其对应的分割掩码被收集到中央服务器上。这种数据聚合在医疗应用中引发了隐私担忧，并且需要大量的集中计算资源。为了解决这些限制，我们研究了联邦学习在隐私保护皮肤病变分割中的可行性。我们使用ISIC 2018皮肤病变分割挑战数据集中的训练集和验证集来模拟分布式学习环境，并开发一个联邦分割模型。所得到的模型在ISIC 2018测试集和PH2数据集上进行评估，以检验其性能和泛化能力。实验结果表明，联邦模型达到了与集中式训练相当的性能，同时始终优于本地训练的模型。这些发现证明了联邦学习在无需集中聚合医学图像的情况下进行协作式皮肤病变分割的潜力。
英文摘要
Skin cancer is a major global health concern, and early detection and accurate lesion delineation are important for effective diagnosis and treatment planning. Automated skin lesion analysis can assist dermatologists, with lesion segmentation serving as a fundamental step in computer-aided diagnostic systems. Conventional deep learning-based segmentation models typically rely on centralized training, where images and their corresponding segmentation masks are collected on a central server. Such data aggregation raises privacy concerns in medical applications and requires substantial centralized computational resources. To address these limitations, we investigate the feasibility of federated learning for privacy-preserving skin lesion segmentation. 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. The resulting model is evaluated on the ISIC 2018 test set and the PH2 dataset to assess its performance and generalizability. Experimental results demonstrate that the federated model achieves performance comparable to centralized training while consistently improving upon the locally trained models. These findings demonstrate the potential of federated learning for collaborative skin lesion segmentation without requiring centralized aggregation of medical images.
