Papillary thyroid cancer pathology image classification method based on deep learning

Abstract

The invention discloses a papillary thyroid cancer pathological image classification method based on deep learning, and mainly solves the problem of poor classification effect on papillary thyroid cancer pathological images in the prior art. According to the scheme, the method comprises the following steps: 1) reading a papillary thyroid cancer pathological section image with an amplification factor of 20, and inputting the papillary thyroid cancer pathological section image into an improved VGG-f convolutional neural network to obtain an attention heat map; 2) normalizing the attention diagram to obtain a discrimination force region position; reading a 40-time amplified thyroid cancer pathological image and obtaining an image block according to the position of the discrimination area; 3)inputting the image blocks into an original VGG-f network, constructing a loss function, and performing supervised training on the network; 4) extracting trained VGG-f network convolution features andperforming classification processing to obtain categories of the image blocks, and 5) judging the categories of the thyroid cancer pathological images according to the categories of the image blocks.The classification accuracy is high, and the method can be used for classifying the thyroid cancer papillary cancer pathological images by a computer.

Description

technical field [0001] The invention belongs to the technical field of image processing, and further relates to an image classification method, which can be used for classification of thyroid cancer pathological images. Background technique [0002] Thyroid cancer is the most common malignant tumor of the endocrine system and the fastest growing malignant tumor in the world. Among the numerous examination methods for thyroid cancer, pathological biopsy is currently the method with the highest sensitivity and specificity. The pathological diagnosis of thyroid cancer must be confirmed by a pathologist observing the stained biopsy under a microscope. Among the four main pathological types of thyroid cancer, papillary thyroid carcinoma accounts for more than 85% of thyroid cancer, and the prognosis of papillary thyroid carcinoma is very good, with a ten-year survival rate as high as 90%. However, the rate of lymphatic metastasis of this type of cancer is as high as 40% to 50%....

AI Technical Summary

Problems solved by technology

[0004] Although the above methods have made great progress in the computer-aided diagnosis of breast cancer, prostate cancer and other diseases, there are few studies on the diagnosis of thyroid cancer.

On the one hand, due to the complexity of pathological images of thyroid cancer, the difference between benign and malignant pathological images is small and the morphology of malignant slices is quite different, and the differences in the slice making process and staining process make the color distribution of pathological slices widely, resulting in the existing The classification model for thyroid cancer pathological images is poorly classified; on the other hand, there is a lack of a large number of publicly available annotated datasets, which makes the existing deep learning classification models unable to learn features well and affects classification accuracy.

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