计算机科学
人工智能
特征(语言学)
机器学习
模式识别(心理学)
上下文图像分类
特征提取
图像(数学)
数据挖掘
语言学
哲学
作者
Guanghui Yue,Peishan Wei,Tianwei Zhou,Youyi Song,Cheng Zhao,Tianfu Wang,Baiying Lei
标识
DOI:10.1109/jbhi.2023.3319516
摘要
Recently, federated learning has become a powerful technique for medical image classification due to its ability to utilize datasets from multiple clinical clients while satisfying privacy constraints. However, there are still some obstacles in federated learning. Firstly, most existing methods directly average the model parameters collected by medical clients on the server, ignoring the specificities of the local models. Secondly, class imbalance is a common issue in medical datasets. In this article, to handle these two challenges, we propose a novel specificity-aware federated learning framework that benefits from an Adaptive Aggregation Mechanism (AdapAM) and a Dynamic Feature Fusion Strategy (DFFS). Considering the specificity of each local model, we set the AdapAM on the server. The AdapAM utilizes reinforcement learning to adaptively weight and aggregate the parameters of local models based on their data distribution and performance feedback for obtaining the global model parameters. For the class imbalance in local datasets, we propose the DFFS to dynamically fuse the features of majority classes based on the imbalance ratio in the min-batch and collaborate the rest of features. We conduct extensive experiments on a dermoscopic dataset and a fundus image dataset. Experimental results show that our method can achieve state-of-the-art results in these two real-world medical applications.
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