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Method for automatically interpreting KI67 pathological section based on deep learning

A deep learning and pathological section technology, applied in the field of machine learning, can solve problems such as dullness, time-consuming and laborious tumor cell counting, and misjudgment by doctors.

Pending Publication Date: 2021-07-30
杭州迪英加科技有限公司
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

However, for clinicians, counting tumor cells in the field of view under the microscope is time-consuming, laborious, and very boring, and doctors are prone to misjudgment due to fatigue and other reasons

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  • Method for automatically interpreting KI67 pathological section based on deep learning
  • Method for automatically interpreting KI67 pathological section based on deep learning
  • Method for automatically interpreting KI67 pathological section based on deep learning

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Embodiment Construction

[0037] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0038] see Figure 1 to Figure 7 , the present invention provides a technical solution: a method for automatically interpreting KI67 pathological slices based on deep learning, comprising the following steps:

[0039] The first step, data collection and doctor’s annotation: collect KI67 slide microscope field of view data, the data volume is not less than 100 pieces; the collection requirements are: all collected data adopt a uniform magnification, such as all...

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Abstract

The invention discloses a method for automatically interpreting a KI67 pathological section based on deep learning. The method comprises the steps: step 1, collecting and marking data; step 2, making a true standard; and dividing all the marked data into a training set, a verification set and a test set; step 3, designing a model architecture and a loss function, and training the model until convergence; step 4, carrying out model prediction and post-processing; step 5, evaluating model performance; and step 6, outputting the analytical model, and visualizing the result. The invention mainly aims to position and discriminate the types of all cells in the visual field of the KI67 section under a microscope, then respectively count positive tumor cells and negative tumor cells, finally calculate the KI67 value, and use the KI67 value as the interpretation standard of the KI67 pathological section.

Description

technical field [0001] The invention relates to the technical field of machine learning, in particular to a method for automatically interpreting KI67 pathological slices based on deep learning. Background technique [0002] In recent years, with the development of artificial intelligence and machine vision technology, digital image processing and artificial intelligence-assisted doctor interpretation of pathological images have become more and more widely used in clinical practice. This method uses a deep learning model, combined with the annotations of experienced doctors, to make high-accuracy interpretation results for pathological data. KI67 is a common immunohistochemical technique in the pathology department. Generally, KI67 index is used as an evaluation index to measure the malignancy of tumors. This index has diagnostic and prognostic value in various cancers. KI67 index can be obtained by analyzing KI67 immunohistochemical pathological images. In clinical practi...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00G06T5/00G06N3/08G16H30/20
CPCG06T7/0012G06N3/08G16H30/20G06T2207/10056G06T2207/30096G06T5/70
Inventor 蔡佳桐杨林祝骋路吴同
Owner 杭州迪英加科技有限公司