轻量级模型精准识别肠腺瘤癌变风险，助力结肠镜实时诊断
UltraLight Med-Vision Mamba for Classification of Neoplastic Progression in Tubular Adenomas
- 基于状态空间模型构建轻量级架构，有效捕捉图像长短期依赖
- 在全切片图像上实现高精度分类，支持实时临床部署
- 适合需要快速、低资源消耗的医学影像分析场景
在常规结肠镜筛查中识别癌前息肉对预防结直肠癌至关重要。先进的深度学习算法可精确分类腺瘤并分层，提升风险评估准确性，支持个性化随访方案，优化患者预后。轻量级医学视觉状态空间模型（Ultralight Med-Vision Mamba）在建模长距离与短距离依赖关系及图像泛化方面表现优异，是分析全切片图像的关键技术。此外，该模型高效的架构在计算速度和可扩展性上具有优势，使其成为实时临床应用的有力工具。
原文摘要 · Abstract (English)
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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