Aiming at the problem of low efficiency of dicentric chromosome identification counting under the microscope, this paper presents a joint processing algorithm combining clustering and watershed. The method first uses clustering and watershed algorithm to segment the original chromosome image, and then identifies the individual chromosomes. The results show that when the equivalent width Y parameter is selected m = 1, n = 1, the true positive rate of dicentric chromosome identification is 76.6%, and positive predictive value is 76.6% in high dose, which is higher than the threshold algorithm for the true positive rate (63.9%) and positive predictive value (63.5%). The number of identified dicentric chromosomes can be used for dose estimation. When 500 cells are used for identification and dose estimation, the dose estimation pass rate can reach 80% in high dose. But for low dose, more cells should be used to identify to increase the dose estimation pass rate.
Model evaluation is critical in deep learning. However, the traditional model evaluation approach is susceptible to issues of untrustworthiness, including insecure data and model sharing, insecure model training, incorrect model evaluation, centralized model evaluation, and evaluation results that can be tampered easily. To minimize these untrustworthiness issues, this paper proposes a blockchain-based model evaluation framework. The framework consists of an access control layer, a storage layer, a model training layer, and a model evaluation layer. The access control layer facilitates secure resource sharing. To achieve fine-grained and flexible access control, an attribute-based access control model combining the idea of a role-based access control model is adopted. A smart contract is designed to manage the access control policies stored in the blockchain ledger. The storage layer ensures efficient and secure storage of resources. Resource files are stored in the IPFS, with the encrypted results of their index addresses recorded in the blockchain ledger. Another smart contract is designed to achieve decentralized and efficient management of resource records. The model training layer performs training on users’ servers, and, to ensure security, the training data must have records in the blockchain. The model evaluation layer utilizes the recorded data to evaluate the recorded models. A method in the smart contract of the storage layer is designed to enable evaluation, with scores automatically uploaded as a resource attribute. The proposed framework is applied to deep learning-based motion object segmentation, demonstrating its key functionalities. Furthermore, we validated the storage strategy adopted by the framework, and the trustworthiness of the framework is also analyzed.
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