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Small sample tumor necrosis rate classification prediction device based on deep learning

A tumor necrosis and deep learning technology, applied in neural learning methods, informatics, image analysis, etc., can solve the problems of the scarcity of tumor samples, the high rate of missed diagnosis and misdiagnosis, and achieve the goal of improving the overall diagnosis level, good survival prognosis and quality of life. Effect

Active Publication Date: 2020-06-12
BEIJING UNIV OF POSTS & TELECOMM +1
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Problems solved by technology

[0005] In view of this, the purpose of the present invention is to propose a small-sample tumor necrosis rate classification and prediction device based on deep learning to solve the problems of the scarcity of tumor samples and the high rate of missed diagnosis and misdiagnosis

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  • Small sample tumor necrosis rate classification prediction device based on deep learning
  • Small sample tumor necrosis rate classification prediction device based on deep learning
  • Small sample tumor necrosis rate classification prediction device based on deep learning

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[0030] In order to make the purpose, technical solutions and advantages of the present disclosure clearer, the present disclosure will be further described in detail below in conjunction with specific embodiments and with reference to the accompanying drawings.

[0031] It should be noted that, unless otherwise defined, the technical terms or scientific terms used in one or more embodiments of the present specification shall have ordinary meanings understood by those skilled in the art to which the present disclosure belongs. "First", "second" and similar words used in one or more embodiments of the present specification do not indicate any order, quantity or importance, but are only used to distinguish different components. "Comprising" or "comprising" and similar words mean that the elements or items appearing before the word include the elements or items listed after the word and their equivalents, without excluding other elements or items. Words such as "connected" or "con...

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Abstract

The invention provides a small sample tumor necrosis rate classification prediction device based on deep learning. The device comprises: a single image generation module configured to input a normal bone contour image and a real pre-chemotherapy tumor image with a tumor necrosis rate category label into a trained first generative adversarial network model to obtain a pre-chemotherapy tumor image with a tumor necrosis rate category label; the time sequence image generation module is configured to input the tumor image before chemotherapy generation into a trained second generative adversarial network model to obtain a tumor generation time sequence image; and the necrosis rate classification module is configured to train a deep convolutional neural network model by adopting the generated tumor time sequence image so as to obtain a tumor necrosis rate classification result of the to-be-detected tumor time sequence image. The device can solve the problems that the number of tumor samplesis small, and the missed diagnosis and misdiagnosis rate is high.

Description

technical field [0001] The present invention relates to the technical field of computer-aided diagnosis, in particular to a small-sample tumor necrosis rate classification and prediction device based on deep learning. Background technique [0002] Primary malignant bone tumors are a group of highly malignant tumors, represented by osteosarcoma, Ewing sarcoma, and undifferentiated sarcoma (malignant fibrous histiocytoma). Among them, the most common osteosarcoma (Osteosarcoma, OS) has an insidious onset, rapid growth, close relationship with neurovascular, and is prone to early distant metastasis. It is a malignant tumor with a fatality rate second only to leukemia in children and adolescents. The tumor stage of patients with osteosarcoma is directly related to their survival prognosis. At least 50% of patients will develop lung metastases within 1 year of diagnosis. The prognosis of patients with lung metastases is extremely poor, with a five-year survival rate of less tha...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T7/13G16H50/20G06N3/04G06N3/08
CPCG06T7/0012G06T7/13G16H50/20G06N3/08G06T2207/20081G06T2207/20084G06T2207/30008G06T2207/30096G06N3/044G06N3/045Y02A90/10
Inventor 牛凯贺志强宋天琪卢阳
Owner BEIJING UNIV OF POSTS & TELECOMM
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