生物信息学2025，Vol.23Issue(1)：61-70,10.DOI:10.12113/202306005
基于联邦半监督学习的皮肤病变智能识别
Federated semi-supervised learning-based intelligent recognition for skin lesion
摘要
Abstract
In recent years,deep learning technology has been widely applied in skin lesion recognition.However,in practical applications,single medical institution faces problems such as limited training data,insufficient labeled samples,and the risk of privacy leakage in centralized learning.To address the above problems,a federated semi-supervised learning-based intelligent recognition mechanism for skin lesion is proposed.Specifically,a federated learning-based cloud-edge collaborative intelligent recognition model for skin lesion is designed,which collaboratively trains data from various medical institutions while protecting the privacy of users.This model can provide users with accurate and convenient diagnostic services.Then,a semi-supervised loss function for heterogeneous data is designed to effectively control the difference between local models and global model.In addition,by combining multiple random samplings and accuracy-based weighting method,the contribution of each local model is clarified,and all uneven local models are aggregated into a global consensus model to further reduce the impact of data heterogeneity.Finally,experimental results show that the proposed mechanism has better performance and scalability than several recently proposed mechanisms.关键词
皮肤病变/深度学习/图像识别/联邦学习Key words
Skin lesion/Deep learning/Image recognition/Federated learning分类
计算机与自动化引用本文复制引用
段聪颖,顾敏杰,李雪,陈思光..基于联邦半监督学习的皮肤病变智能识别[J].生物信息学,2025,23(1):61-70,10.基金项目
国家自然科学基金(No.61971235) （No.61971235）
中国博士后科学基金项目(No.2018M630590) （No.2018M630590）
江苏省"333高层次人才培养工程"、江苏省博士后科研资助计划(No.2021K501C) （No.2021K501C）
南京市妇幼保健院青年人才和南京邮电大学'1311'人才计划资助. （）
