ArticleBiomedical engineering online2023
A YOLO-based AI system for classifying calcifications on spot magnification mammograms.
Article in Biomedical engineering online, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic literature review on mammography: deep learning techniques for breast cancer detection with global and Asian perspectives.BMC cancer · 2025Pooled it
- Deep learning model for automated identification of ventrally positioned right hepatic artery in contrast-enhanced computed tomography of pediatric congenital biliary dilatation: development and clinical application.Pediatric radiology · 2026Article
- Development and validation of radiomics models for the diagnoses of breast lesions with calcification on ultrasound.Quantitative imaging in medicine and surgery · 2026Article
- Development and evaluation of deep learning models for detecting and classifying various bone tumours in full-field limb radiographs using automated object detection models.Bone & joint research · 2025Article
- AI-Based Cancer Detection Model for Contrast-Enhanced Mammography.Bioengineering (Basel, Switzerland) · 2023Article
- A Multi-label Artificial Intelligence Approach for Improving Breast Cancer Detection With Mammographic Image Analysis.In vivo (Athens, Greece)Article
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Abstract
objectivesUse of an AI system based on deep learning to investigate whether the system can aid in distinguishing malignant from benign calcifications on spot magnification mammograms, thus potentially reducing unnecessary biopsies.
methodsIn this retrospective study, we included public and in-house datasets with annotations for the calcifications on both craniocaudal and mediolateral oblique vies, or both craniocaudal and mediolateral views of each case of mammograms. All the lesions had pathological results for correlation. Our system comprised an algorithm based on You Only Look Once (YOLO) named adaptive multiscale decision fusion module. The algorithm was pre-trained on a public dataset, Curated Breast Imaging Subset of Digital Database for Screening Mammography (CBIS-DDSM), then re-trained and tested on the in-house dataset of spot magnification mammograms. The performance of the system was investigated by receiver operating characteristic (ROC) analysis.
resultsWe included 1872 images from 753 calcification cases (414 benign and 339 malignant) from CBIS-DDSM. From the in-house dataset, 636 cases (432 benign and 204 malignant) with 1269 spot magnification mammograms were included, with all lesions being recommended for biopsy by radiologists. The area under the ROC curve for our system on the in-house testing dataset was 0.888 (95% CI 0.868-0.908), with a sensitivity of 88.4% (95% CI 86.9-8.99%), specificity of 80.8% (95% CI 77.6-84%), and an accuracy of 84.6% (95% CI 81.8-87.4%) at the optimal cutoff value. Using the system with two views of spot magnification mammograms, 80.8% benign biopsies could be avoided.
conclusionThe AI system showed good accuracy for classification of calcifications on spot magnification mammograms which were all categorized as suspicious by radiologists, thereby potentially reducing unnecessary biopsies.
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