Journal of Jilin University(Engineering and Technology Edition) ›› 2021, Vol. 51 ›› Issue (6): 2164-2173.doi: 10.13229/j.cnki.jdxbgxb20200674

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COVID⁃19 chest CT image segmentation based on federated learning and blockchain

Sheng-sheng WANG(),Jing-yu CHEN,Yi-nan LU()   

  1. College of Computer Science and Technology，Jilin University，Changchun 130012，China
  • Received:2020-09-03 Online:2021-11-01 Published:2021-11-15
  • Contact: Yi-nan LU E-mail:wss@jlu.edu.cn;luyn@jlu.edu.cn

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Abstract:

This paper proposed a COVID-19 chest CT image segmentation method based on Federated Learning (FL) and blockchain to automatically segment the area in lung affected by COVID-19. Firstly, in the situation where the sample data of patients is limited and it is distributed in different institutions, which cannot be easily collected, we applied FL method. Then, we used blockchain network to replace the central server in FL to solve the “single point of failure” problem. Finally, we designed a lightweight separable convolution U-NET to reduce the cost of computation and time. Experimental results show that the method has good performance after training, and its dice metric can achieve 63.26%, which is helpful for diagnosis of COVID-19.

Key words: computer application technology, blockchain, federated learning, Corona Virus Disease 2019, image segmentation

CLC Number: 

  • TP39

Fig.1

Traditional framework of federated learning"

Fig.2

One iteration process for BCFL"

Fig.3

Schematic diagram of bifurcation"

Fig.4

Architecture of separable convolution U-Net"

Fig.5

Two samples of the COVID-19 chest CTsegmentation dataset"

Fig.6

Distribution of training samples（N=360） in local device（ND=5）"

Table 1

Testing results"

方法DiceSensitivitySpecificity
U-Net集中训练65.5869.2194.34
联邦学习62.4765.8192.67
BCFL63.2665.6693.14

Fig.7

Learning curves for three methods"

Fig.8

Partial prediction results"

Fig.9

Relationship between the learning completionlatency and block generation rate λ"

Fig.10

Learning completion latency in thecase of miners' malfunction"

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