ArticleJournal of personalized medicine2020
Automated Breast Cancer Detection in Digital Mammograms of Various Densities via Deep Learning.
Article in Journal of personalized medicine, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers, 2 of them syntheses that pooled it.
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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
35 citing papers in PubMed, 2 syntheses or guidelines pooled it, 123 citations in OpenAlex.
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Mammography diagnosis of breast cancer screening through machine learning: a systematic review and meta-analysis.Clinical and experimental medicine · 2023Pooled it
- AI-Assisted Tumor Boundary Delineation via Targeted Ultrasmall Iron Oxide Nanoprobe for High-Contrast HER2-Positive Tumor Imaging.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- A review of application of Artificial Intelligence in breast cancer detection and treatment.Discover oncology · 2026Review
- Deep-learning-based breast cancer stage prediction from H&E-stained whole-slide images in resource-constrained settings.Journal of pathology informatics · 2026Article
- Ensemble-based high-performance deep learning models for medical image retrieval in breast cancer detection.Scientific reports · 2026Article
- Artificial intelligence for breast cancer management.Communications medicine · 2026Review
- Enhancing detection of previously missed non-palpable breast carcinomas through artificial intelligence.European journal of radiology open · 2025Article
- AI in 2D Mammography: Improving Breast Cancer Screening Accuracy.Medicina (Kaunas, Lithuania) · 2025Article
- Multi-center study: ultrasound-based deep learning features for predicting Ki-67 expression in breast cancer.Scientific reports · 2025Article
- Artificial Intelligence in Cancer Diagnosis: A Game-changer in Healthcare.Current pharmaceutical biotechnology · 2025Review
- (KAUH-BCMD) dataset: advancing mammographic breast cancer classification with multi-fusion preprocessing and residual depth-wise network.Frontiers in big data · 2025Article
- Development of a machine learning predictive model for early detection of breast cancer.F1000Research · 2025Article
- Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications.Heliyon · 2024Article
- Reproducibility and Explainability of Deep Learning in Mammography: A Systematic Review of Literature.The Indian journal of radiology & imaging · 2024Review
- Screening mammography performance according to breast density: a comparison between radiologists versus standalone intelligence detection.Breast cancer research : BCR · 2024Article
- Breast cancer detection employing stacked ensemble model with convolutional features.Cancer biomarkers : section A of Disease markers · 2024Article
- Breast Ultrasound Images Augmentation and Segmentation Using GAN with Identity Block and Modified U-Net 3.Sensors (Basel, Switzerland) · 2023Article
- Enhancement Technique Based on the Breast Density Level for Mammogram for Computer-Aided Diagnosis.Bioengineering (Basel, Switzerland) · 2023Article
- Efficient Breast Cancer Diagnosis from Complex Mammographic Images Using Deep Convolutional Neural Network.Computational intelligence and neuroscience · 2023Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
Abstract
Mammography plays an important role in screening breast cancer among females, and artificial intelligence has enabled the automated detection of diseases on medical images. This study aimed to develop a deep learning model detecting breast cancer in digital mammograms of various densities and to evaluate the model performance compared to previous studies. From 1501 subjects who underwent digital mammography between February 2007 and May 2015, craniocaudal and mediolateral view mammograms were included and concatenated for each breast, ultimately producing 3002 merged images. Two convolutional neural networks were trained to detect any malignant lesion on the merged images. The performances were tested using 301 merged images from 284 subjects and compared to a meta-analysis including 12 previous deep learning studies. The mean area under the receiver-operating characteristic curve (AUC) for detecting breast cancer in each merged mammogram was 0.952 ± 0.005 by DenseNet-169 and 0.954 ± 0.020 by EfficientNet-B5, respectively. The performance for malignancy detection decreased as breast density increased (density A, mean AUC = 0.984 vs. density D, mean AUC = 0.902 by DenseNet-169). When patients' age was used as a covariate for malignancy detection, the performance showed little change (mean AUC, 0.953 ± 0.005). The mean sensitivity and specificity of the DenseNet-169 (87 and 88%, respectively) surpassed the mean values (81 and 82%, respectively) obtained in a meta-analysis. Deep learning would work efficiently in screening breast cancer in digital mammograms of various densities, which could be maximized in breasts with lower parenchyma density.
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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.