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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2508.09339 |
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| _version_ | 1866908487585366016 |
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| author | Sultana, Aqsa Abouzahra, Nordin Rahu, Ahmed Shula, Brian Combs, Brandon Forchetti, Derrick Aspiras, Theus Asari, Vijayan K. |
| author_facet | Sultana, Aqsa Abouzahra, Nordin Rahu, Ahmed Shula, Brian Combs, Brandon Forchetti, Derrick Aspiras, Theus Asari, Vijayan K. |
| contents | Identification of precancerous polyps during routine colonoscopy screenings is vital for their excision, lowering the risk of developing colorectal cancer. Advanced deep learning algorithms enable precise adenoma classification and stratification, improving risk assessment accuracy and enabling personalized surveillance protocols that optimize patient outcomes. Ultralight Med-Vision Mamba, a state-space based model (SSM), has excelled in modeling long- and short-range dependencies and image generalization, critical factors for analyzing whole slide images. Furthermore, Ultralight Med-Vision Mamba's efficient architecture offers advantages in both computational speed and scalability, making it a promising tool for real-time clinical deployment. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09339 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas Sultana, Aqsa Abouzahra, Nordin Rahu, Ahmed Shula, Brian Combs, Brandon Forchetti, Derrick Aspiras, Theus Asari, Vijayan K. Computer Vision and Pattern Recognition Identification of precancerous polyps during routine colonoscopy screenings is vital for their excision, lowering the risk of developing colorectal cancer. Advanced deep learning algorithms enable precise adenoma classification and stratification, improving risk assessment accuracy and enabling personalized surveillance protocols that optimize patient outcomes. Ultralight Med-Vision Mamba, a state-space based model (SSM), has excelled in modeling long- and short-range dependencies and image generalization, critical factors for analyzing whole slide images. Furthermore, Ultralight Med-Vision Mamba's efficient architecture offers advantages in both computational speed and scalability, making it a promising tool for real-time clinical deployment. |
| title | UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.09339 |