Medical Image Collaborative Training Based on MultiBlockchain In this paper, we introduce a novel privacypreserving decentralized collaborative training approach for medical image analysis, based on a multiBlockchain system. With this approach, researchers from different medical institutions can collaboratively train machine learning models without sharing sensitive patient data. A partialparameter propagation approach is applied to prevent indirect patient information leakage during model propagation. Based on peertopeer communication in a multiBlockchain system, one training task can benefit from other tasks in other Blockchains, according to their task similarity. Furthermore, a personalized model for each institution is learned after collaborative training. Compared with training models by gathering all participantsu0027 datasets, experiment results show that our approach achieves similar performance while protecting sensitive patient data. Transferring the trained model from other Blockchains is also proved to be effective in accelerating model convergence. The personalized model process further improves model accuracy.

BlockchainBased Access Control Schemes for Medical Image Analysis System. Medical image analysis systems with machine learning have played an important role in the computeraided diagnosis and treatment for diseases. However, individual privacy of user data is vulnerable since the training data is exposed to unauthorized user. Therefore, this paper designs an access control scheme to prevent illegal users from accessing medical data while achieving high accuracy of lesion classification. Specifically, in the novel lightweight consortium blockchainbased access control scheme, a chosen consortium node is utilized as key generation center instead of a trusted third party in conventional schemes. Two public retinal datasets are utilized for the classification of diabetic retinopathy (DR). Security analysis shows that the proposed scheme can prevent the user data from leakage and malicious tampering. Experimental results demonstrate that the processing of data cleaning is efficient to increase the accuracy of the classification for early lesions of DR by removing low quality images, and the accuracy is up to 90.2%.