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

A prediction method and brain tumor technology, applied in the field of multi-center prediction of brain tumor prognosis and survival based on federated learning, can solve the problems of a single global model and do not consider the differences in data distribution of different centers, so as to improve accuracy and improve communication High efficiency and high accuracy

Pending Publication Date: 2021-10-29
AFFILIATED HUSN HOSPITAL OF FUDAN UNIV
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Problems solved by technology

[0004] However, existing federated learning methods usually adopt a single global model to obtain the shared knowledge of all users onl

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

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Embodiment Construction

[0037] The present invention will be described in detail below in conjunction with specific embodiments. The following examples will help those skilled in the art to further understand the present invention, but do not limit the present invention in any form. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all belong to the protection scope of the present invention.

[0038] A multi-center prediction method for brain tumor prognosis and survival based on federated learning, refer to figure 1 , including the following steps:

[0039] Step S1: Construct a federated learning model based on the multi-center federated learning client-server architecture (as shown in 2). The data from one client is not enough to train a well-performing neural network, therefore, a federated learning framework based on an active learning strategy is designed to minimize the total loss acro...

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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 ...

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Application Information

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IPC IPC(8): G16H70/60G16H30/00G16H50/70G16H10/60G06N20/00
CPCG16H70/60G16H30/00G16H50/70G16H10/60G06N20/00
Inventor 于泽宽耿道颖项睿刘晓李郁欣陈卫强李强尹波张军杜鹏
Owner AFFILIATED HUSN HOSPITAL OF FUDAN UNIV
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