Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning
Accurate and early breast cancer detection is critical for improving patient outcomes. In this study, we propose PatchCascade-ViT, a novel self-supervised Vision Transformer (ViT) framework for automated BI-RADS classification of mammographic images. Unlike conventional deep learning approaches that...
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| Format: | Article |
| Language: | English |
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11045361/ |
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| author | Abdelrahman Abdallah Mahmoud Salaheldin Kasem Ibrahim Abdelhalim Norah Saleh Alghamdi Ayman El-Baz |
| author_facet | Abdelrahman Abdallah Mahmoud Salaheldin Kasem Ibrahim Abdelhalim Norah Saleh Alghamdi Ayman El-Baz |
| author_sort | Abdelrahman Abdallah |
| collection | DOAJ |
| description | Accurate and early breast cancer detection is critical for improving patient outcomes. In this study, we propose PatchCascade-ViT, a novel self-supervised Vision Transformer (ViT) framework for automated BI-RADS classification of mammographic images. Unlike conventional deep learning approaches that rely heavily on annotated datasets, PatchCascade-ViT leverages Self Patch-level Supervision (SPS) to learn meaningful mammographic representations from unlabeled data, significantly enhancing classification performance. Our framework operates through a two-stage cascade classification process. In the first stage, the model differentiates non-cancerous from potentially cancerous mammograms using SelfPatch, an innovative self-supervised learning task that enhances patch-level feature learning by enforcing consistency among spatially correlated patches. The second stage refines the classification by distinguishing Scattered Fibroglandular from Heterogeneously and Extremely Dense breast tissue categories, enabling more precise breast cancer risk assessment. To validate the effectiveness of PatchCascade-ViT, we conducted extensive evaluations on a dataset of 4,368 mammograms across three BI-RADS classes. Our method achieved a system sensitivity of 85.01% and an F1-score of 84.90%, outperforming existing deep learning-based approaches. By integrating self-supervised learning with a cascade vision transformer architecture, PatchCascade-ViT reduces reliance on annotated datasets while maintaining high classification accuracy. These findings demonstrate its potential for enhancing breast cancer screening, aiding radiologists in early detection, and improving clinical decision-making. |
| format | Article |
| id | doaj-art-7aadcf898b054c4b9d0e66c5fbfe552c |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-7aadcf898b054c4b9d0e66c5fbfe552c2025-08-20T03:40:59ZengIEEEIEEE Access2169-35362025-01-011313550013551410.1109/ACCESS.2025.358158211045361Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade LearningAbdelrahman Abdallah0https://orcid.org/0000-0001-8747-4927Mahmoud Salaheldin Kasem1https://orcid.org/0000-0002-8513-570XIbrahim Abdelhalim2https://orcid.org/0009-0000-1544-7276Norah Saleh Alghamdi3https://orcid.org/0000-0001-6421-6001Ayman El-Baz4https://orcid.org/0000-0001-7264-1323Department of Information Technology, Assiut University, Assiut, EgyptDepartment of Computer Science, University of Innsbruck, Innsbruck, AustriaDepartment of Bioengineering, University of Louisville, Louisville, KY, USADepartment of Computer Sciences, Princess Nourah bint Abdulrahman University, Riyadh, Saudi ArabiaDepartment of Bioengineering, University of Louisville, Louisville, KY, USAAccurate and early breast cancer detection is critical for improving patient outcomes. In this study, we propose PatchCascade-ViT, a novel self-supervised Vision Transformer (ViT) framework for automated BI-RADS classification of mammographic images. Unlike conventional deep learning approaches that rely heavily on annotated datasets, PatchCascade-ViT leverages Self Patch-level Supervision (SPS) to learn meaningful mammographic representations from unlabeled data, significantly enhancing classification performance. Our framework operates through a two-stage cascade classification process. In the first stage, the model differentiates non-cancerous from potentially cancerous mammograms using SelfPatch, an innovative self-supervised learning task that enhances patch-level feature learning by enforcing consistency among spatially correlated patches. The second stage refines the classification by distinguishing Scattered Fibroglandular from Heterogeneously and Extremely Dense breast tissue categories, enabling more precise breast cancer risk assessment. To validate the effectiveness of PatchCascade-ViT, we conducted extensive evaluations on a dataset of 4,368 mammograms across three BI-RADS classes. Our method achieved a system sensitivity of 85.01% and an F1-score of 84.90%, outperforming existing deep learning-based approaches. By integrating self-supervised learning with a cascade vision transformer architecture, PatchCascade-ViT reduces reliance on annotated datasets while maintaining high classification accuracy. These findings demonstrate its potential for enhancing breast cancer screening, aiding radiologists in early detection, and improving clinical decision-making.https://ieeexplore.ieee.org/document/11045361/Breast cancerBI-RADS classificationself-supervised learning (SSL)vision transformers (ViTs)cascade classification |
| spellingShingle | Abdelrahman Abdallah Mahmoud Salaheldin Kasem Ibrahim Abdelhalim Norah Saleh Alghamdi Ayman El-Baz Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning IEEE Access Breast cancer BI-RADS classification self-supervised learning (SSL) vision transformers (ViTs) cascade classification |
| title | Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning |
| title_full | Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning |
| title_fullStr | Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning |
| title_full_unstemmed | Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning |
| title_short | Improving BI-RADS Mammographic Classification With Self-Supervised Vision Transformers and Cascade Learning |
| title_sort | improving bi rads mammographic classification with self supervised vision transformers and cascade learning |
| topic | Breast cancer BI-RADS classification self-supervised learning (SSL) vision transformers (ViTs) cascade classification |
| url | https://ieeexplore.ieee.org/document/11045361/ |
| work_keys_str_mv | AT abdelrahmanabdallah improvingbiradsmammographicclassificationwithselfsupervisedvisiontransformersandcascadelearning AT mahmoudsalaheldinkasem improvingbiradsmammographicclassificationwithselfsupervisedvisiontransformersandcascadelearning AT ibrahimabdelhalim improvingbiradsmammographicclassificationwithselfsupervisedvisiontransformersandcascadelearning AT norahsalehalghamdi improvingbiradsmammographicclassificationwithselfsupervisedvisiontransformersandcascadelearning AT aymanelbaz improvingbiradsmammographicclassificationwithselfsupervisedvisiontransformersandcascadelearning |