Breast lesion risk assessment system based on deep learning

Abstract

The invention discloses a breast lesion risk assessment system based on deep learning, and relates to the fields of deep learning, computer vision and medical imaging. According to the method, a trans-unet structure is provided, the global connection between coding parts is enhanced by using Transforms, so that the extracted image features are more characterized, an attention mechanism is integrated, the extraction of image detail features is facilitated, and then through the combination of shallow features and deep features, the feature expression is more sufficient, and the extraction efficiency is improved. The small target can be segmented and depicted more accurately; a new model scaling method is integrated in a classification model OfficientNet, a group of composite coefficients are obtained firstly based on a neural network structure search technology, and then the structure of the network is determined, so that the OfficientNet is faster than other networks and has good fitting degree with data, and the precision is higher.

Description

technical field [0001] The invention relates to the fields of deep learning, computer vision, and medical imaging, in particular to a breast lesion risk assessment system based on deep learning. Background technique [0002] Breast cancer remains a thorny disease worldwide. As the second most common female disease in the world, it once killed more than 1 million women worldwide in a single year. Therefore, in order to be able to detect breast cancer and prevent it at an early stage, the 5-year survival rate of early breast cancer can reach 98-100%. Therefore, many countries now recommend the use of X-ray mammography screening (the gold standard for breast cancer). Mammography screening is the simplest non-invasive detection method with good repeatability. It can be carried out according to the mammography images of different periods. Before and after comparison. However, the screening method of mammography is not perfect, and there are significant false positives, which wi...

AI Technical Summary

Problems solved by technology

[0005] Breast cancer is mainly divided into three grades: the first grade indicates that the tumor cells are relatively well differentiated, and the prognosis of the patient is relatively good; the second grade indicates that the uncertainty is relatively large, that is, some people may have a better prognosis or poorer prognosis ; The third grade is that the shape of tumor cells is quite different from that of normal cells, that is, poorly differentiated, and the ability to proliferate is particularly strong. Such patients may have poor prognosis

And such a judgment is too vague and puts too much psychological pressure on the patient, which is not conducive to the recovery of the patient.

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