OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis
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arXiv
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| Main Authors: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| author | Ahmed, Istiak Ahmed, Galib Sanjid, K. Shahriar Hossain, Md. Tanzim Khan, Md. Nishan Khan, Md. Misbah Rahman, Md. Arifur Haque, Sheikh Anisul Rupa, Sharmin Akhtar Mia, Mohammed Mejbahuddin Kamal, Mahmud Hasan Mostofa Sarker, Md. Mostafa Kamal Uddin, M. Monir |
| author_facet | Ahmed, Istiak Ahmed, Galib Sanjid, K. Shahriar Hossain, Md. Tanzim Khan, Md. Nishan Khan, Md. Misbah Rahman, Md. Arifur Haque, Sheikh Anisul Rupa, Sharmin Akhtar Mia, Mohammed Mejbahuddin Kamal, Mahmud Hasan Mostofa Sarker, Md. Mostafa Kamal Uddin, M. Monir |
| contents | OncoVision is a multimodal AI pipeline that combines mammography images and clinical data for better breast cancer diagnosis. Employing an attention-based encoder-decoder backbone, it jointly segments four ROIs - masses, calcifications, axillary findings, and breast tissues - with state-of-the-art accuracy and robustly predicts ten structured clinical features: mass morphology, calcification type, ACR breast density, and BI-RADS categories. To fuse imaging and clinical insights, we developed two late-fusion strategies. By utilizing complementary multimodal data, late fusion strategies improve diagnostic precision and reduce inter-observer variability. Operationalized as a secure, user-friendly web application, OncoVision produces structured reports with dual-confidence scoring and attention-weighted visualizations for real-time diagnostic support to improve clinician trust and facilitate medical teaching. It can be easily incorporated into the clinic, making screening available in underprivileged areas around the world, such as rural South Asia. Combining accurate segmentation with clinical intuition, OncoVision raises the bar for AI-based mammography, offering a scalable and equitable solution to detect breast cancer at an earlier stage and enhancing treatment through timely interventions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_19667 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis Ahmed, Istiak Ahmed, Galib Sanjid, K. Shahriar Hossain, Md. Tanzim Khan, Md. Nishan Khan, Md. Misbah Rahman, Md. Arifur Haque, Sheikh Anisul Rupa, Sharmin Akhtar Mia, Mohammed Mejbahuddin Kamal, Mahmud Hasan Mostofa Sarker, Md. Mostafa Kamal Uddin, M. Monir Computer Vision and Pattern Recognition OncoVision is a multimodal AI pipeline that combines mammography images and clinical data for better breast cancer diagnosis. Employing an attention-based encoder-decoder backbone, it jointly segments four ROIs - masses, calcifications, axillary findings, and breast tissues - with state-of-the-art accuracy and robustly predicts ten structured clinical features: mass morphology, calcification type, ACR breast density, and BI-RADS categories. To fuse imaging and clinical insights, we developed two late-fusion strategies. By utilizing complementary multimodal data, late fusion strategies improve diagnostic precision and reduce inter-observer variability. Operationalized as a secure, user-friendly web application, OncoVision produces structured reports with dual-confidence scoring and attention-weighted visualizations for real-time diagnostic support to improve clinician trust and facilitate medical teaching. It can be easily incorporated into the clinic, making screening available in underprivileged areas around the world, such as rural South Asia. Combining accurate segmentation with clinical intuition, OncoVision raises the bar for AI-based mammography, offering a scalable and equitable solution to detect breast cancer at an earlier stage and enhancing treatment through timely interventions. |
| title | OncoVision: Integrating Mammography and Clinical Data through Attention-Driven Multimodal AI for Enhanced Breast Cancer Diagnosis |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2511.19667 |