ArticleRadiology. Artificial intelligence2025
High-Performance Open-Source AI for Breast Cancer Detection and Localization in MRI.
Article in Radiology. Artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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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
2 citing papers in PubMed.
- Foundation models in biomedical imaging: turning hype into reality.Nature biomedical engineering · 2026Review
- Machine learning-based classification of histological subtypes of invasive breast cancer using MRI contralateral breast texture features.Scientific reports · 2025Article
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8 authors.
Funding
Abstract
Purpose To develop and evaluate an open-source deep learning model for detection and localization of breast cancer on MRI scans. Materials and Methods In this retrospective study, a deep learning model for breast cancer detection and localization was trained on the largest breast MRI dataset to date. Data included all breast MRI examinations conducted at a tertiary cancer center in the United States between 2002 and 2019. The model was validated on sagittal MRI scans from the primary site (
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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.