Area-based breast percentage density estimation in mammograms using weight-adaptive multitask learning.

ABSTRACT: Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial for computing the breast density. Semiautomatic and fully automatic computer-aided design tools have been developed to estimate the percentage of breast density in mammograms. However, the available approaches are usually limited to specific mammogram views and are inadequate for complete delineation of the pectoral muscle. These tools also perform poorly in cases of data variability and often require an experienced radiologist to adjust the segmentation threshold for fibroglandular tissue within the breast area. This study proposes a new deep learning ar

SUBMITTER: Gudhe NR 

PROVIDER: S-EPMC9283472 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature