Personalized federated medical image classification with adaptive transfer robust features
Objective Patient data cannot be shared among medical institutions due to medical data confidentiality regula-tions,considerably limiting data scale.Federated learning ensures that all clients can train local models and aggregate global models in a decentralized manner without sharing data.However,the heterogeneity of medical data substantially affects the aggregation and deployment of global models in federated learning.In most federated learning methods,the aggregation of global model parameters is achieved by multiplying the fixed weight with the local model parameters and then summing them.The local model personalization method requires a large number of manual experiments to select the appro-priate model layer for personalization construction.Although these methods can realize the aggregation of global models or the construction of personalized local models,they cannot automatically aggregate global model parameters and construct personalized local models.Moreover,they lack pertinence to heterogeneity characteristics.Therefore,an adaptive person-alized federated learning algorithm via feature transfer(APFFT)is proposed.This algorithm can automatically identify and select robust features for personalized local model construction and global model aggregation.It can also suppress and filter heterogeneous feature information.Method To construct a personalized local model,a robust feature selection network(RFS-Net)was proposed in this study.RFS-Net can automatically identify and select features by calculating transfer weights and the amount of feature transfer on the basis of model representation.When transferring features from a global model to a local model,RFS-Net constructs transfer loss functions on the basis of transfer weights and the amount of feature transfer to constrain the local model and strengthen its attention toward effective transfer features.In the aggregation of the global model,the adaptive aggregation network(AA-Net)was proposed to transfer features from the local model to the global model.AA-Net updated the transfer weight and constructed the aggregation loss on the basis of the cross-entropy change of the global model for filtering the heterogeneity feature information of each local model.In this study,PyTorch was used to build and train the models,while ResNet18 was used for the convolutional neural network(CNN)structure.RFS-Net and AA-Net were composed of fully connected,pooling,softmax,and ReLU6 layers.The parameters of RFS-Net,AA-Net,and the CNN were updated via stochastic gradient descent with a momentum of 0.9.Experiments were con-ducted on three medical image datasets:the nonpublic dataset of pulmonary adenocarcinoma and tuberculosis classifica-tion,the public dataset Camelyon17,and the public dataset LIDC.The dataset of pulmonary adenocarcinoma tuberculosis classification came from 5 hospitals,with 1 009 cases.Among which,Center 1(training set n=260,test setn=242),Center 2(training set n=34,test set n=54),Center 3(training set n=39,test set n=40),Center 4(training set n=145,test set n=108),and Center 5(training set n=36,test set n=51)were used in the experiment.The learning rate and decay rate of RFS-Net and AA-Net were both 0.000 1,while the learning rate and decay rate of the CNN were 0.001 and 0.000 5,respectively.Focal loss was used to calculate cross-entropy.In addition,gender,age,and nodule size in clinical information are of considerable reference value in the diagnosis of tuberculosis and lung adenocarcinoma.There-fore,we provided statistics for this information,and the results showed that in Center 2,the overall age and nodule size were small,while in Center 4,the overall nodule size was large,exhibiting a certain gap with the global average level.Camelyon17 was composed of 450 000 histological images from 5 hospitals.In the experiment,the learning rate and decay rate of the CNN,RFS-Net,and AA-Net were all 0.000 1.Standard cross-entropy was used to constrain CNN training.LIDC data came from 7 research institutions and 8 medical image companies,with 1018 cases.Lesions with Grades 1 to 2 malignancies were classified as benign,while those with Grades 4 to 5 malignancies were classified as malignant.Finally,1 746 lesions were included in the dataset to simulate the federated learning application scenario.The lesions were then randomly divided into 4 centers in accordance with the cases.Center 1(training set n=254,test set n=169),Center 2(training set n=263,test set n=190),Center 3(training set n=305,test set n=124),and Center 4(training set n=247,test set n=194)were used in the experiment.The learning rate and decay rate of RFS-Net and AA-Net were both 0.000 1.The learning rate and decay rate of the CNN were 0.001 and 0.000 1,respectively.The cross-entropy loss was calculated using standard cross-entropy.Result Three types of medical image classification tasks were compared with four existing methods.The evaluation indexes included receiver operating characteristic(ROC)and accuracy.The experimen-tal results showed that in the tuberculosis lung adenocarcinoma classification task,the center test sets of the end-to-end area under the ROC curve(AUC)were 0.791 5,0.798 1,0.76,0.705 7,and 0.806 9.In the breast cancer histological image classification task,the center test sets of end-to-end accuracy were 0.984 9,0.980 8,0.983 5,0.982 6,and 0.983 4.In the pulmonary nodule benign and malignancy classification task,the center test sets of the end-to-end AUC were 0.809 7,0.849 8,0.784 8,and 0.792 3.Conclusion The federated learning method proposed in this study can reduce the influence of heterogeneous characteristics and realize the adaptive construction of personalized local models and the adaptive aggregation of global models.The results show that our model is superior to several existing federated learning methods,and model performance is considerably improved.
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