ArticleIEEE transactions on medical imaging2020
Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening.
Article in IEEE transactions on medical imaging, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 187 papers, 3 of them syntheses that pooled it.
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Who cites it
187 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Artificial Intelligence Applications in Medical Devices for Personalized Health Care Solutions: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Performance of artificial intelligence in breast cancer screening programmes: a systematic review.BMJ open · 2025Pooled it
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Side-level versus patient-level evaluation in four-view mammography classification: a comprehensive benchmark on the CMMD dataset.Future science OA · 2026Article
- Optimizing AI-assisted decision-making in mammography screening: focusing on uncertainties.Insights into imaging · 2026Article
- A novel hierarchical transfer learning to enhance breast cancer detection on screening mammography using deep learning.Physical and engineering sciences in medicine · 2026Article
- Deep meta-learning framework with U-Net segmentation and optimization-based feature selection for breast cancer detection in MRI images.Quantitative imaging in medicine and surgery · 2026Article
- Subcategorisation of Data for AI Models in Healthcare: A Case Study in Mammography.Diagnostics (Basel, Switzerland) · 2026Article
- Hybrid metaheuristic feature selection for breast cancer detection in digital mammography: a radiomics and deep learning pilot feasibility study.BMC medical imaging · 2026Article
- Histopathological Assessment of Myocardial Ischemia-Reperfusion Injury Using Transformer-Based Artificial Intelligence: Model Comparison Study.JMIR medical informatics · 2026Article
- Deep-learning based adaptive fusion of CC and MLO views for improved mammographic cancer diagnosis.MethodsX · 2026Article
- Enhancing Early Detection of Contralateral Breast Cancer in Breast Cancer Survivors Using AI-Assisted Mammography.Annals of surgical oncology · 2026Article
- AI-assisted basal cell carcinoma diagnosis with LC-OCT: A multicentric retrospective study.Journal of the European Academy of Dermatology and Venereology : JEADV · 2026Article
- A deep learning system for non-invasive breast cancer diagnosis with multimodal data.Nature biomedical engineering · 2026Article
- Multimodal multi-instance learning for cardiopulmonary exercise testing performance prediction.NPJ digital medicine · 2026Article
- Evaluating deep learning models for pancreatic cancer diagnosis.Clinical and experimental medicine · 2026Article
- Evaluation of the Diagnostic Accuracy of Comercially Available AI-CAD Solution in Mammography Screening in Mexican Women (Mammo-MX Database).Diagnostics (Basel, Switzerland) · 2026Article
- Combined use of two artificial intelligence-based algorithms for mammography triaging: a retrospective simulation study.European radiology · 2026Article
- Organ cross-talk: molecular mechanisms, biological functions, and therapeutic interventions for diseases.Signal transduction and targeted therapy · 2026Review
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
127 more citing papers are in PubMed but not listed here.
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Authors and funding
32 authors.
Funding
Abstract
We present a deep convolutional neural network for breast cancer screening exam classification, trained, and evaluated on over 200000 exams (over 1000000 images). Our network achieves an AUC of 0.895 in predicting the presence of cancer in the breast, when tested on the screening population. We attribute the high accuracy to a few technical advances. 1) Our network's novel two-stage architecture and training procedure, which allows us to use a high-capacity patch-level network to learn from pixel-level labels alongside a network learning from macroscopic breast-level labels. 2) A custom ResNet-based network used as a building block of our model, whose balance of depth and width is optimized for high-resolution medical images. 3) Pretraining the network on screening BI-RADS classification, a related task with more noisy labels. 4) Combining multiple input views in an optimal way among a number of possible choices. To validate our model, we conducted a reader study with 14 readers, each reading 720 screening mammogram exams, and show that our model is as accurate as experienced radiologists when presented with the same data. We also show that a hybrid model, averaging the probability of malignancy predicted by a radiologist with a prediction of our neural network, is more accurate than either of the two separately. To further understand our results, we conduct a thorough analysis of our network's performance on different subpopulations of the screening population, the model's design, training procedure, errors, and properties of its internal representations. Our best models are publicly available at https://github.com/nyukat/breast_cancer_classifier.
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