Mammography, or breast X-ray imaging, is the most widely used imaging modality to detect cancer and other breast diseases. Recent studies have shown that deep learning-based computer-assisted detection and diagnosis (CADe/x) tools have been developed to support physicians and improve the accuracy of interpreting mammography. A number of large-scale mammography datasets from different populations with various associated annotations and clinical data have been introduced to study the potential of learning-based methods in the field of breast radiology. With the aim to develop more robust and more interpretable support systems in breast imaging, we introduce VinDr-Mammo, a Vietnamese dataset of digital mammography with breast-level assessment and extensive lesion-level annotations, enhancing the diversity of the publicly available mammography data. The dataset consists of 5,000 mammography exams, each of which has four standard views and is double read with disagreement (if any) being resolved by arbitration. The purpose of this dataset is to assess Breast Imaging Reporting and Data System (BI-RADS) and breast density at the individual breast level. In addition, the dataset also provides the category, location, and BI-RADS assessment of non-benign findings. We make VinDr-Mammo publicly available as a new imaging resource to promote advances in developing CADe/x tools for mammography interpretation.
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http://dx.doi.org/10.1038/s41597-023-02100-7 | DOI Listing |
AJR Am J Roentgenol
January 2025
Department of Surgery, the Netherlands Cancer Institute, Amsterdam, the Netherlands.
AJR Am J Roentgenol
January 2025
UPMC Magee-Womens Hospital of UPMC, Pittsburgh, PA, USA 15213.
AJR Am J Roentgenol
January 2025
Department of Radiology, Division of Breast Imaging and Intervention, Mayo Clinic, Phoenix, AZ.
Contrast-enhanced mammography (CEM) is growing in clinical use due to its increased sensitivity and specificity compared to full-field digital mammography (FFDM) and/or digital breast tomosynthesis (DBT), particularly in patients with dense breasts. To perform an intraindividual comparison of MGD between FFDM, DBT, a combination protocol using both FFDM and DBT (combined FFDM-DBT), and CEM, in patients undergoing breast cancer screening. This retrospective study included 389 women (median age, 57.
View Article and Find Full Text PDFInt J Comput Assist Radiol Surg
January 2025
Pattern Recognition Lab, Friedrich-Alexander-University Erlangen-Nuremberg, Martensstr. 3, 91058, Erlangen, Bayern, Germany.
Purpose: Breast cancer remains one of the most prevalent cancers globally, necessitating effective early screening and diagnosis. This study investigates the effectiveness and generalizability of our recently proposed data augmentation technique, attention-guided erasing (AGE), across various transfer learning classification tasks for breast abnormality classification in mammography.
Methods: AGE utilizes attention head visualizations from DINO self-supervised pretraining to weakly localize regions of interest (ROI) in images.
Eur J Radiol Open
June 2025
Radiology Department, National Cancer Institute, Cairo University, Egypt.
Purpose: To investigate the impact of artificial intelligence (AI) reading digital mammograms in increasing the chance of detecting missed breast cancer, by studying the AI- flagged early morphology indictors, overlooked by the radiologist, and correlating them with the missed cancer pathology types.
Methods And Materials: Mammograms done in 2020-2023, presenting breast carcinomas (n = 1998), were analyzed in concordance with the prior one year's result (2019-2022) assumed negative or benign. Present mammograms reviewed for the descriptors: asymmetry, distortion, mass, and microcalcifications.
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