UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas
This work addresses the need for accurate and real-time identification of precancerous polyps during colonoscopy screenings to optimize patient outcomes, representing an incremental advancement in medical imaging.
The paper tackles the problem of classifying neoplastic progression in tubular adenomas from colonoscopy images to improve colorectal cancer risk assessment, achieving precise classification and stratification with an efficient state-space model that excels in modeling dependencies and image generalization.
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.