UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas

• URL: http://arxiv.org/abs/2508.09339v1
• Date: Tue, 12 Aug 2025 20:56:31 GMT
• Title: UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas
• Authors: Aqsa Sultana, Nordin Abouzahra, Ahmed Rahu, Brian Shula, Brandon Combs, Derrick Forchetti, Theus Aspiras, Vijayan K. Asari,
• Abstract summary: Ultralight Med-Vision Mamba is a state-space based model (SSM)<n>It has excelled in modeling long- and short-range dependencies and image generalization.<n>It is a promising tool for real-time clinical deployment.
• Score: 2.7861537812259316
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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