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A medical image classification method, model training method and server

一种医学图像、模型的技术，应用在医学图像、图像分析、神经学习方法等方向，能够解决混入质量、成本高、效率低下等问题

Active Publication Date: 2021-02-05
TENCENT TECH (SHENZHEN) CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In this way, not only is it inefficient, but it is also very easy to mix in low-quality or invalid data
The existing microscope image annotation technology has low annotation reliability, high cost and long cycle to obtain valid data

Method used

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  • A medical image classification method, model training method and server
  • A medical image classification method, model training method and server
  • A medical image classification method, model training method and server

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Embodiment Construction

[0061] The following description provides specific details for a thorough understanding and practice of various embodiments of the present disclosure. It should be understood by those skilled in the art that the technical solutions of the present disclosure may be practiced without some of these details. In some instances, well-known structures and functions have not been shown or described in detail to avoid unnecessarily obscuring the description of the embodiments of the present disclosure. Terms used in this disclosure are to be interpreted in their broadest reasonable manner, even though they are used in connection with specific embodiments of this disclosure.

[0062] First of all, some terms involved in the embodiments of the present disclosure will be described to facilitate the understanding of those skilled in the art.

[0063] Out of focus: When the microscope objective lens focuses on the pathological section, or the camera fails to reach or exceed the reasonable ...

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Abstract

The present invention mainly relates to the technical field of deep learning of artificial intelligence. This paper describes methods for medical image classification, model training methods, and servers. The medical image classification method includes: obtaining a data set of medical images; performing quality analysis on the data set of medical images to extract feature information of medical images, and the quality analysis includes color gamut-saturation-brightness analysis, sharpness analysis, texture One or more of analysis and entropy analysis; based on the extracted feature information, using a pre-trained deep learning network for abnormal detection and classification of medical images to classify the medical images to obtain classification results, the classification includes One or more of classifying whether the medical image is normal tissue, irrelevant tissue, lens out of focus, or white balance failure.

Description

technical field [0001] The invention relates to the technical field of deep learning of artificial intelligence, in particular to a classification method of microscope images, a model training method and a server. Background technique [0002] The solutions provided in the embodiments of the present application involve technologies such as deep learning of artificial intelligence. Artificial Intelligence (AI) is a theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results. In other words, artificial intelligence is a comprehensive technique of computer science that attempts to understand the nature of intelligence and produce a new kind of intelligent machine that can respond in a similar way to human intelligence. Artificial intelligence is to study the design principles an...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06T7/90G06T7/155G06T7/13G06T7/00G06T5/00G06T3/40G06N3/08G06N3/04
CPCG06T7/155G06T7/13G06T3/4007G06T7/90G06T7/0012G06N3/08G06T2207/30168G06N3/045G06T5/70G06T1/20G06T2207/10056G06T2207/20081G06T2207/20084G06T2207/30024G06T2207/30096G06T2207/20076G16H50/70G16H50/20G16H30/40G16H30/20G06T2207/30004
Inventor 肖凯文韩骁叶虎周昵昀
Owner TENCENT TECH (SHENZHEN) CO LTD
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