PURPOSE: Breast density is a widely established independent breast cancer risk factor. With the increasing utilization of digital breast tomosynthesis (DBT) in breast cancer screening, there is an opportunity to estimate volumetric breast density (VB...
BACKGROUND/AIM: Contrast-enhanced mammography (CEM) is a relatively novel imaging technique that enables both anatomical and functional breast imaging, with improved diagnostic performance compared to standard 2D mammography. The aim of this study is...
Machine learning models for the diagnosis of breast cancer can facilitate the prediction of cancer risk and subsequent patient management among other clinical tasks. For the models to impact clinical practice, they ought to follow standard workflows,...
Canadian Association of Radiologists journal = Journal l'Association canadienne des radiologistes
Dec 3, 2024
The integration of Digital Breast Tomosynthesis (DBT) and Artificial Intelligence (AI) represents a significant advance in breast cancer screening. This combination aims to address several challenges inherent in traditional screening while promising ...
PURPOSE: There are many radiological datasets for breast cancer, some which have supported the development of AI medical devices for breast cancer screening and image classification. This review aims to identify mammography datasets (including digiti...
Early diagnosis and treatment of breast cancer can effectively reduce mortality. Since mammogram is one of the most commonly used methods in the early diagnosis of breast cancer, the classification of mammogram images is an important work of computer...
Breast mass segmentation plays a crucial role in early breast cancer detection and diagnosis, and while Convolutional Neural Networks (CNN) have been widely used for this task, their reliance on local receptive fields limits ability to capture long-r...
BACKGROUND: The presence of a blurred area, depending on its localization, in a mammogram can limit diagnostic accuracy. The goal of this study was to develop a model for automatic detection of blur in diagnostically relevant locations in digital mam...
Journal of the American College of Radiology : JACR
Nov 17, 2024
OBJECTIVE: To demonstrate and test the capabilities of the ACR Connect and AI-LAB software platform by implementing multi-institutional artificial intelligence (AI) training and validation for breast density classification.
Accessible mammography datasets and innovative machine learning techniques are at the forefront of computer-aided breast cancer diagnosis. However, the opacity surrounding private datasets and the unclear methodology behind the selection of subset im...
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