Publications by authors named "Sarah D Verboom"
Article Synopsis
- The study aimed to see if the order in which mammograms are read, based on their characteristics, could help radiologists spot abnormalities more efficiently, particularly focusing on factors like breast density and visual adaptation.
- A total of 150 mammograms were analyzed by 13 radiologists using three reading methods: random order, increasing volumetric breast density (VBD), and self-supervised learning (SSL) grouping.
- Results indicated that readings organized by increasing VBD led to slightly better performance and reduced reading time compared to random order, without affecting sensitivity and specificity significantly.
Purpose: We developed a segmentation method suited for both raw (for processing) and processed (for presentation) digital mammograms (DMs) that is designed to generalize across images acquired with systems from different vendors and across the two standard screening views.
Approach: A U-Net was trained to segment mammograms into background, breast, and pectoral muscle. Eight different datasets, including two previously published public sets and six sets of DMs from as many different vendors, were used, totaling 322 screen film mammograms (SFMs) and 4251 DMs (2821 raw/processed pairs and 1430 only processed) from 1077 different women.