Deep learning-based breast region segmentation in raw and processed digital mammograms: generalization across views and vendors
Author:
Affiliation:
1. Radboud University Medical Center, Department of Medical Imaging, Nijmegen, The Netherlands
2. Radboud University Medical Center, Department for Health Evidence, Nijmegen, The Netherlands
Publisher
SPIE-Intl Soc Optical Eng
Subject
Radiology, Nuclear Medicine and imaging
Reference20 articles.
1. Deep-LIBRA: An artificial-intelligence method for robust quantification of breast density with independent validation in breast cancer risk assessment
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3. A deep learning system to obtain the optimal parameters for a threshold-based breast and dense tissue segmentation
4. Parenchymal texture analysis in digital mammography: A fully automated pipeline for breast cancer risk assessment
5. Improved Threshold Based and Trainable Fully Automated Segmentation for Breast Cancer Boundary and Pectoral Muscle in Mammogram Images
Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
1. Deep learning-based mammographic breast compression pressure estimates on processed images vs an unprocessed image reference;17th International Workshop on Breast Imaging (IWBI 2024);2024-05-29
2. Breast Delineation in Full-Field Digital Mammography Using the Segment Anything Model;Diagnostics;2024-05-15
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