Method and device for fully convolutional single-stage breast image lesion detection based on multiple images

Abstract

Aiming at the fact that the existing breast lesion detection algorithm cannot well combine the information of bilateral breasts, cannot simultaneously meet the needs of identification and detection of multiple diseases including lumps and calcifications, and has a general effect on asymmetric dense glands, the present invention proposes A fully convolutional single-stage mammography lesion detection method with fusion of multi-image information. Use a non-anchor-based method for lesion detection, extract features of different scales from the original image, fuse the features of different scales, and fuse the information of different images, and finally directly predict whether a point on the feature map corresponds to a lesion and the specific location of the lesion.

Description

technical field

[0001] The present invention relates to the field of image processing, in particular to a processing method for detecting lesion regions in mammary gland images. Background technique

[0002] Breast cancer is the malignant tumor with the highest incidence rate in women. There are more than 270,000 new breast cancer cases in my country every year, and the incidence of breast cancer is increasing year by year, seriously threatening women's health. Early diagnosis of breast cancer is very important, early accurate diagnosis can increase the 5-year survival rate of breast cancer patients from 25% to 99%. Screening mammography is considered the test of choice for breast cancer screening. At present, mammography relies on subjective diagnosis, and the overall accuracy rate is not high enough and is limited by the level of evaluators. Compared with the personal experience judgment of a medical expert, the artificial intelligence recognition algorithm may identify ...

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