Multi-Path Deep Learning Model for Automated Mammographic Density Categorization

Breast density is one of the strongest risk factors for breast cancer. Our purpose of this study is to develop a deep learning model for BI-RADS density classification on digital mammograms (DM). With IRB approval, 2581 DMs were retrospectively collected from 672 women in our institution. We designed a multi-path DCNN (MP-DCNN) to classify each DM into one of four BI-RADS density categories. The MP-DCNN has four inputs: (1) subsampled DM (800 mu m pixel spacing), (2) a mask of dense area (MDA) obtained with a U-net (800 mu m pixel spacing), (3) the largest square region of interest (ROI) within mammographic breast (100 mu m pixel spacing), and ( 4) automated percentage of breast density (PD). As the baseline statistic, a single path DCNN with subsampled DM (800 um pixel spacing) as input was used. An experienced Mammography Quality Standards Act (MQSA) radiologist provided BI-RADS density category and PD by interactive thresholding as the reference standards. With ten-fold cross-validation, the BI-RADS categories by MP-DCNN for 2068 of the 2581 cases agreed with radiologist's assessment (accuracy = 80.7%, weighted kappa = 0.83) and the accuracy reached 89.0% if the breasts were categorized as non-dense (BI-RADS A & B) and dense (BI-RADS C & D). For comparison, a single path DCNN as the baseline model obtained agreement in 1906 of the 2581 cases (accuracy = 73.8%, weighted kappa = 0.75). The improvement in BI-RADS classification from the baseline to the MP-DCNN was statistically significant (p<0.001).