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

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 ...

AI Technical Summary

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 inability to distinguish borderline follicular tumors

And due to the lack of a large amount of clinical data, there is currently no convolutional neural network method for the histopathological image diagnosis of follicular thyroid tumors.

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