High Precision Mammography Lesion Identification From Imprecise Medical Annotations.

ABSTRACT: Breast cancer screening using Mammography serves as the earliest defense against breast cancer, revealing anomalous tissue years before it can be detected through physical screening. Despite the use of high resolution radiography, the presence of densely overlapping patterns challenges the consistency of human-driven diagnosis and drives interest in leveraging state-of-art localization ability of deep convolutional neural networks (DCNN). The growing availability of digitized clinical archives enables the training of deep segmentation models, but training using the most widely available form of coarse hand-drawn annotations works against learning the precise boundary of cancerous tissue in evaluation, while producing results that are more aligned with the annotations rather than the underl

SUBMITTER: An U 

PROVIDER: S-EPMC8716325 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature