ArticleJournal of medical imaging (Bellingham, Wash.)2023
Malmö Breast ImaginG database: objectives and development.
Article in Journal of medical imaging (Bellingham, Wash.), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed, 4 citations in OpenAlex.
- Subcategorisation of Data for AI Models in Healthcare: A Case Study in Mammography.Diagnostics (Basel, Switzerland) · 2026Article
- Assessing the robustness of AI lesion risk scores at different exposure settings using an anthropomorphic breast phantom.Radiation protection dosimetry · 2026Article
- Annotation and characterization of lesions in breast tomosynthesis images.Radiation protection dosimetry · 2026Article
- A framework for simulation of temporal evolution and longitudinal studies of breast anatomy in radiology.Medical physics · 2025Article
- Assessing mammographic density change within individuals across screening rounds using deep learning-based software.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Our journey toward implementation of digital breast tomosynthesis in breast cancer screening: the Malmö Breast Tomosynthesis Screening Project.Journal of medical imaging (Bellingham, Wash.) · 2025Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Purpose: We describe the design and implementation of the Malmö Breast ImaginG (M-BIG) database, which will support research projects investigating various aspects of current and future breast cancer screening programs. Specifically, M-BIG will provide clinical data to:1.investigate the effect of breast cancer screening on breast cancer prognosis and mortality;2.develop and validate the use of artificial intelligence and machine learning in breast image interpretation; and3.develop and validate image-based radiological breast cancer risk profiles. Approach: The M-BIG database is intended to include a wide range of digital mammography (DM) and digital breast tomosynthesis (DBT) examinations performed on women at the Mammography Clinic in Malmö, Sweden, from the introduction of DM in 2004 through 2020. Subjects may be included multiple times and for diverse reasons. The image data are linked to extensive clinical, diagnostic, and demographic data from several registries. Results: To date, the database contains a total of 451,054 examinations from 104,791 women. During the inclusion period, 95,258 unique women were screened. A total of 19,968 examinations were performed using DBT, whereas the rest used DM. Conclusions: We describe the design and implementation of the M-BIG database as a representative and accessible medical image database linked to various types of medical data. Work is ongoing to add features and curate the existing data.
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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.