ArticleScientific reports2018
Detecting and classifying lesions in mammograms with Deep Learning.
Article in Scientific reports, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 2 of them syntheses that pooled it.
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Who cites it
173 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.Clinical and experimental medicine · 2023Pooled it
- Breast Cancer Detection and Diagnosis Using Mammographic Data: Systematic Review.Journal of medical Internet research · 2019Pooled it
- A novel hierarchical transfer learning to enhance breast cancer detection on screening mammography using deep learning.Physical and engineering sciences in medicine · 2026Article
- Between automation and alienation: rethinking AI's role in radiologist well-being.European radiology · 2026Article
- Interpretable Bayesian Modeling for Multireader Multicase Studies: Addressing Overdispersion and Limited Sample Size in Diagnostic Enhancement Evaluation.Statistics in medicine · 2026Article
- Reducing false positives in breast cancer screening with computer-aided detection.Clinical and experimental medicine · 2026Article
- MetaChest: generalized few-shot learning of pathologies from chest X-rays.Visual computing for industry, biomedicine, and art · 2026Article
- Skeletal Muscle Mass Estimation from Lower Leg Digital Images Using Machine Learning.Progress in rehabilitation medicine · 2026Article
- Explainable Deep Learning for Breast Lesion Classification in Digital and Contrast-Enhanced Mammography.Diagnostics (Basel, Switzerland) · 2025Article
- Development of an Automated CAD System for Lesion Detection in DCE-MRI.Journal of imaging informatics in medicine · 2025Article
- Enhancing breast cancer detection on screening mammogram using self-supervised learning and a hybrid deep model of Swin Transformer and convolutional neural networks.Journal of medical imaging (Bellingham, Wash.) · 2025Article
- Enhancing Breast Lesion Detection in Mammograms via Transfer Learning.Journal of imaging · 2025Article
- AI for Lesion Detection in Musculoskeletal Radiology.Journal of the Korean Society of Radiology · 2025Review
- Innovative Multi-View Strategies for AI-Assisted Breast Cancer Detection in Mammography.Journal of imaging · 2025Article
- Predicting Very Early-Stage Breast Cancer in BI-RADS 3 Lesions of Large Population with Deep Learning.Journal of imaging · 2025Article
- CLASEG: advanced multiclassification and segmentation for differential diagnosis of oral lesions using deep learning.Scientific reports · 2025Article
- Frozen Large-Scale Pretrained Vision-Language Models are the Effective Foundational Backbone for Multimodal Breast Cancer Prediction.IEEE journal of biomedical and health informatics · 2025Article
- Hybrid transformer-based model for mammogram classification by integrating prior and current images.Medical physics · 2025Article
- Subgroup evaluation to understand performance gaps in deep learning-based classification of regions of interest on mammography.PLOS digital health · 2025Article
- Axillary lymph node metastasis in breast cancer: from historical axillary surgery to updated advances in the preoperative diagnosis and axillary management.BMC surgery · 2025Review
113 more citing papers are in PubMed but not listed here.
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In the last two decades, Computer Aided Detection (CAD) systems were developed to help radiologists analyse screening mammograms, however benefits of current CAD technologies appear to be contradictory, therefore they should be improved to be ultimately considered useful. Since 2012, deep convolutional neural networks (CNN) have been a tremendous success in image recognition, reaching human performance. These methods have greatly surpassed the traditional approaches, which are similar to currently used CAD solutions. Deep CNN-s have the potential to revolutionize medical image analysis. We propose a CAD system based on one of the most successful object detection frameworks, Faster R-CNN. The system detects and classifies malignant or benign lesions on a mammogram without any human intervention. The proposed method sets the state of the art classification performance on the public INbreast database, AUC = 0.95. The approach described here has achieved 2nd place in the Digital Mammography DREAM Challenge with AUC = 0.85. When used as a detector, the system reaches high sensitivity with very few false positive marks per image on the INbreast dataset. Source code, the trained model and an OsiriX plugin are published online at https://github.com/riblidezso/frcnn_cad .
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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.