Method for recognizing lesion type and gene mutation in thyroid tumor pathological image

A technology of pathological images and thyroid, applied in the field of image processing, can solve the problems of inability to distinguish borderline follicular tumors, limited diagnostic ability, lack of clinical data, etc., achieve high robustness, high sensitivity and specificity, and improve accuracy sexual effect

Pending Publication Date: 2021-05-28
PEKING UNION MEDICAL COLLEGE HOSPITAL CHINESE ACAD OF MEDICAL SCI
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Problems solved by technology

[0012] Existing convolutional neural network methods can be used in the imaging diagnosis of thyroid follicular adenoma and thyroid cancer, but these existing methods have problems such as low accuracy, limited diagnostic ability, and i

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  • Method for recognizing lesion type and gene mutation in thyroid tumor pathological image
  • Method for recognizing lesion type and gene mutation in thyroid tumor pathological image
  • Method for recognizing lesion type and gene mutation in thyroid tumor pathological image

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[0044] The principle and spirit of the present invention will be described below with reference to several exemplary embodiments. It should be understood that these embodiments are given only to enable those skilled in the art to better understand and implement the present invention, rather than to limit the scope of the present invention in any way.

[0045] DenseNet is a widely used convolutional neural network network structure, which is characterized by alleviating the problem of gradient disappearance through dense connections, strengthening feature propagation, and reducing the number of parameters. The input of each layer in the neural network is the union of the outputs of all previous layers, and the feature map learned by this layer will be directly passed to all subsequent layers as input.

[0046] DenseNet improves the efficiency of the network by reducing the amount of calculation of each layer and feature multiplexing. By letting the input of the first layer dire...

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Abstract

The invention discloses a method for recognizing a lesion area in a thyroid follicular tumor tissue pathological image, and predicts gene mutation. The method is an automatic auxiliary diagnosis technology based on a deep learning method, a lesion area in a thyroid follicular tumor pathological tissue slice image is automatically positioned by using big data and a deep convolutional neural network algorithm, and a pathological histological type and a gene mutation type of the lesion area are automatically recognized. According to the pathological tissue image, recognizing cases simultaneously carrying RAS and other driver gene mutations, and realizing histological classification of the follicular thyroid tumor and prediction of related gene mutations. According to the method, information is provided for clinicians, pathological diagnosis and clinical decision making are assisted, and development of digital pathology and precise medical treatment is promoted.

Description

technical field [0001] The invention relates to the technical field of image processing, in particular to a method for identifying lesion areas in pathological images of follicular thyroid tumors, and classifying the gene mutation types. Background technique [0002] The thyroid produces thyroid hormones, for example thyroxine and triiodothyronine are two active thyroid hormones produced by the thyroid. They play a vital role in controlling the body's metabolism, including protein production, thermoregulation, and energy production and regulation, among others. [0003] Thyroid disease is the second largest disease in the field of endocrine, and thyroid cancer is also a common malignant tumor in the field of endocrine. It is divided into papillary thyroid cancer, follicular thyroid cancer, poorly differentiated thyroid cancer and anaplastic thyroid cancer. [0004] Follicular neoplasms of the thyroid are a group of nodular thyroid neoplasms with a follicular growth pattern ...

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Application Information

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IPC IPC(8): G06T7/00G06K9/32G06K9/62G06N3/04
CPCG06T7/0012G06T2207/30096G06V10/25G06N3/045G06F18/24323
Inventor 梁智勇陈浩张卉胡羽吴焕文林黄靖
Owner PEKING UNION MEDICAL COLLEGE HOSPITAL CHINESE ACAD OF MEDICAL SCI
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