DBT (Digital Breast Tomosynthesis) image lump automatic detection method based on Faster R-CNN (Faster Region-based Convolution Neural Network)

Abstract

The invention discloses a DBT (Digital Breast Tomosynthesis) image lump automatic detection method based on a Faster R-CNN (Faster Region-based Convolution Neural Network). According to method, lump automatic detection is carried out through utilization of an advanced Faster R-CNN; a new feature extraction network structure is designed; deep features are integrated; the deep features of DBT imagetargets are mined; model parameters are shared; the trained model parameters are reduced; the network overfitting degree can be mitigated; a detection effect is relatively good; and the detection average precision is improved.

Description

technical field [0001] The technology of the present invention relates to the field of medical imaging and image recognition technology, and in particular to a method for automatic detection of tumors in digital breast tomographic images based on FasterRCNN deep learning. Background technique [0002] In recent years, the incidence of breast cancer in my country has been increasing year by year, ranking first among female malignant tumors. Mammography is an important method for early detection of breast cancer. Digital Breast Tomosynthesis (DBT) can overcome the tissue overlap problem in Full-field digital mammography (FFDM) and improve the detection rate of breast cancer. Especially for the dense breasts common in Asian women, DBT can significantly improve the accuracy of diagnosis, reduce unnecessary biopsies caused by "false positives", reduce the retest rate and the cost of breast cancer screening. Because the use of three-dimensional tomographic imaging technology sig...

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Problems solved by technology

Obtain the image to be detected after preprocessing

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