Multi-center brain tumor prognosis lifetime prediction method and system based on federated learning

Abstract

The invention provides a multi-center brain tumor prognosis lifetime prediction method and system based on federated learning, and brain tumor prognosis lifetime prediction is carried out by using multi-scale information such as a multi-center multi-mode brain tumor image and omics information thereof, clinical medical record information of a patient and the like. The invention provides a multi-center federated learning mechanism based on active learning and reinforcement learning. According to the invention, a comprehensive brain tumor prognosis lifetime classification model is established by combining the patient electronic medical record information stored in each center in a distributed manner with the radiomics features and the deep learning features, and a reliable brain tumor prognosis lifetime prediction system with higher accuracy is realized on the basis of ensuring patient image data privacy.

Description

technical field [0001] The present invention relates to the field of medical image-aided diagnosis, in particular to a multi-center federated learning-based method and system for predicting the prognosis and survival of brain tumors. Background technique [0002] Brain tumors are common tumors in the human body. The prevalence of brain tumors in my country is about 32 / 100,000, accounting for 6.31% of the overall tumor incidence, including gliomas, lymphomas, and metastatic tumors. In the era of medical digitalization, according to the needs of clinical diagnosis and treatment of tumors, use brain tumor multimodal MRI scans to accurately segment different subregions of brain tumors, such as edema, necrotic core, enhancing and non-enhancing tumors It has important clinical significance for the diagnosis, prognosis and treatment of brain tumors. At present, radiologists mainly conduct subjective and qualitative graded diagnosis of brain tumors in the examination results based ...

AI Technical Summary

Problems solved by technology

[0004] However, existing federated learning methods usually adopt a single global model to obtain the shared knowledge of all users only by aggregating individual client model parameters, without considering the differences between data distributions in different centers

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