ArticleRadiology. Artificial intelligence2023
Transformer-based Deep Neural Network for Breast Cancer Classification on Digital Breast Tomosynthesis Images.
Article in Radiology. Artificial intelligence, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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13 citing papers in PubMed.
- MSF-Swin-ICFNet: An Image-Derived Multi-Scale Swin Transformer Framework with Cross-Scale Feature Fusion for Breast Lesion Classification and Segmentation in Mammograms.Tomography (Ann Arbor, Mich.) · 2026Article
- ViT-FuseNet: Same-Patient MRI-Pathology Feature Fusion for Multimodal Breast Cancer Diagnosis.Journal of clinical medicine · 2026Article
- Benign Share Benefit to Malignant: Balanced Mixing on Feature Space for Imbalanced Breast Cancer Classification.Bioengineering (Basel, Switzerland) · 2026Article
- Evaluating deep learning models for pancreatic cancer diagnosis.Clinical and experimental medicine · 2026Article
- Fusion of genomic and pathological data for breast cancer detection using BCDNN.Frontiers in medicine · 2026Article
- Deep Learning in Digital Breast Tomosynthesis: Current Status, Challenges, and Future Trends.MedComm · 2025Review
- Large multimodality model fine-tuned for detecting breast and esophageal carcinomas on CT: a preliminary study.Japanese journal of radiology · 2025Article
- Principal component analysis and fine-tuned vision transformation integrating model explainability for breast cancer prediction.Visual computing for industry, biomedicine, and art · 2025Article
- Liquid biopsy in cancer diagnosis and prognosis: a paradigm shift in precision oncology.Frontiers in molecular biosciences · 2025Review
- Combining shallow and deep neural networks on pseudo-color enhanced images for digital breast tomosynthesis lesion classification.Frontiers in digital health · 2025Article
- Detection of breast cancer in digital breast tomosynthesis with vision transformers.Scientific reports · 2024Article
- SIFT-DBT: SELF-SUPERVISED INITIALIZATION AND FINE-TUNING FOR IMBALANCED DIGITAL BREAST TOMOSYNTHESIS IMAGE CLASSIFICATION.Proceedings. IEEE International Symposium on Biomedical Imaging · 2024Article
- Impact of AI for Digital Breast Tomosynthesis on Breast Cancer Detection and Interpretation Time.Radiology. Artificial intelligence · 2024Article
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Authors and funding
6 authors.
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Abstract
Purpose: To develop an efficient deep neural network model that incorporates context from neighboring image sections to detect breast cancer on digital breast tomosynthesis (DBT) images. Materials and Methods: The authors adopted a transformer architecture that analyzes neighboring sections of the DBT stack. The proposed method was compared with two baselines: an architecture based on three-dimensional (3D) convolutions and a two-dimensional model that analyzes each section individually. The models were trained with 5174 four-view DBT studies, validated with 1000 four-view DBT studies, and tested on 655 four-view DBT studies, which were retrospectively collected from nine institutions in the United States through an external entity. Methods were compared using area under the receiver operating characteristic curve (AUC), sensitivity at a fixed specificity, and specificity at a fixed sensitivity. Results: On the test set of 655 DBT studies, both 3D models showed higher classification performance than did the per-section baseline model. The proposed transformer-based model showed a significant increase in AUC (0.88 vs 0.91, Conclusion: A transformer-based deep neural network using data from neighboring sections improved breast cancer classification performance compared with a per-section baseline model and was more efficient than a model using 3D convolutions.
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