ArticleDiagnostics (Basel, Switzerland)2023
Vision-Transformer-Based Transfer Learning for Mammogram Classification.
Article in Diagnostics (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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Who cites it
36 citing papers in PubMed, 100 citations in OpenAlex.
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- Integrating Spatial Proteogenomics in Cancer Research.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Review
- Intelligent identification of osteoporosis on hip X-rays using vision transformer.Bone reports · 2026Article
- Development and evaluation of a multistage transfer learning framework for robust medical image analysis.Scientific reports · 2026Article
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
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- A deep learning and radiomics fusion model enhances endoscopic ultrasonography diagnosis of gastric tumors.Frontiers in oncology · 2026Article
- Breast cancer classification based on microcalcifications using dual branch vision transformer fusion.Scientific reports · 2025Article
- Article
- SwinCAMF-Net: Explainable Cross-Attention Multimodal Swin Network for Mammogram Analysis.Diagnostics (Basel, Switzerland) · 2025Article
- From Machine Learning to Ensemble Approaches: A Systematic Review of Mammogram Classification Methods.Diagnostics (Basel, Switzerland) · 2025Review
- BCECNN: an explainable deep ensemble architecture for accurate diagnosis of breast cancer.BMC medical informatics and decision making · 2025Article
- Innovative Multi-View Strategies for AI-Assisted Breast Cancer Detection in Mammography.Journal of imaging · 2025Article
- Breaking Diagnostic Barriers: Vision Transformers Redefine Monkeypox Detection.Diagnostics (Basel, Switzerland) · 2025Article
- Revolutionizing gastroenterology and hepatology with artificial intelligence: From precision diagnosis to equitable healthcare through interdisciplinary practice.World journal of gastroenterology · 2025Review
- Comparative analysis of convolutional neural networks and vision transformers in identifying benign and malignant breast lesions.Medicine · 2025Article
- The reliability of deep learning models in assessing the shoulder arthroscopic field's visual clarity in relation to bleeding.JSES international · 2025Article
- Predicting bone metastasis risk of colorectal tumors using radiomics and deep learning ViT model.Journal of bone oncology · 2025Article
Corrections and comments
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Authors and funding
10 authors at 8 institutions in 3 countries.
Funding
Abstract
Breast mass identification is a crucial procedure during mammogram-based early breast cancer diagnosis. However, it is difficult to determine whether a breast lump is benign or cancerous at early stages. Convolutional neural networks (CNNs) have been used to solve this problem and have provided useful advancements. However, CNNs focus only on a certain portion of the mammogram while ignoring the remaining and present computational complexity because of multiple convolutions. Recently, vision transformers have been developed as a technique to overcome such limitations of CNNs, ensuring better or comparable performance in natural image classification. However, the utility of this technique has not been thoroughly investigated in the medical image domain. In this study, we developed a transfer learning technique based on vision transformers to classify breast mass mammograms. The area under the receiver operating curve of the new model was estimated as 1 ± 0, thus outperforming the CNN-based transfer-learning models and vision transformer models trained from scratch. The technique can, hence, be applied in a clinical setting, to improve the early diagnosis of breast cancer.
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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.