ArticleBMC research notes2026
Toward general AI harmonization with real mammogram imaging breast cancer dataset.
Article in BMC research notes, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
3 citing papers in PubMed.
- Multi-Parametric Ultrasound Radiomic Kinetics with Machine Learning Ensemble for Early Prediction of Pathologic Response to Neoadjuvant Chemotherapy in Breast Cancer.Diagnostics (Basel, Switzerland) · 2026Article
- Toward general AI harmonization with real mammogram imaging breast cancer dataset.BMC research notes · 2026Article
- BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer.BMC medical informatics and decision making · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesThis dataset was specifically collected to support the development and evaluation of computer-aided diagnosis (CAD) systems for breast cancer detection. The dataset, Ma’amon Diagnostic Center Mammogram Imaging for Breast Cancer (MDCMI-BC), was created to provide high-quality mammogram images with expert annotations to improve AI-based diagnostic tools. This data is intended to aid in the validation of CAD systems, ensuring they can more effectively detect breast cancer by integrating with other available multimodal datasets. DATA DESCRIPTION: The MDCMI-BC dataset contains 133 mammogram cases with 266 images, categorized by the BI-RADS (Breast Imaging-Reporting and Data System) classification. These include 100 normal, 66 benign, and 100 malignant cases, with images captured from both left and right sides using craniocaudal (CC) and mediolateral oblique (MLO) views. The dataset includes detailed annotations made by expert radiologists, which provide ground truth (GT) for training and validating CAD systems. The images are converted from DICOM format to PNG, de-identified to ensure patient privacy, and made available for public access at the Mendeley repository. The data will assist in refining diagnostic algorithms and fostering AI technologies in breast cancer detection.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.