Accurate risk stratification of precancerous polyps during routine colonoscopy screenings is essential for lowering the risk of developing colorectal cancer (CRC). However, assessment of low-grade dysplasia remains limited by subjective histopathologic interpretation. Advancements in digital pathology and deep learning provide new opportunities to identify subtle and fine morphologic patterns associated with malignant progression that may be imperceptible to the human eye. In this work, we propose XtraLight-MedMamba, an ultra-lightweight state-space-based deep learning framework for classifying neoplastic tubular adenomas from whole-slide images (WSIs). The architecture is a blend of ConvNext based shallow feature extractor with parallel vision mamba to efficiently model both long- and short-range dependencies and image generalization. An integration of Spatial and Channel Attention Bridge (SCAB) module enhances multiscale feature extraction, while Fixed Non-Negative Orthogonal Classifier (FNOClassifier) enables substantial parameter reduction and improved generalization. The model was evaluated on a curated dataset acquired from patients with low-grade tubular adenomas, stratified into case and control cohorts based on subsequent CRC development. XtraLight-MedMamba achieved an accuracy of 97.18% and an F1-score of 0.9767 using approximately 32,000 parameters, outperforming transformer-based and conventional Mamba architectures with significantly higher model complexity.

翻译：在常规结肠镜筛查中，对癌前息肉进行准确的风险分层对于降低结直肠癌（CRC）的发病风险至关重要。然而，对低级别异型增生的评估仍受限于主观的组织病理学判读。数字病理学和深度学习的进展为识别与恶性进展相关的、人眼可能难以察觉的细微形态学模式提供了新的机遇。本研究提出XtraLight-MedMamba，一种基于状态空间的超轻量级深度学习框架，用于从全切片图像（WSIs）中分类肿瘤性管状腺瘤。该架构融合了基于ConvNext的浅层特征提取器与并行视觉Mamba，以高效建模长程和短程依赖关系并提升图像泛化能力。空间与通道注意力桥接（SCAB）模块的集成增强了多尺度特征提取，而固定非负正交分类器（FNOClassifier）则实现了参数的大幅减少并改善了泛化性能。该模型在一个精心构建的数据集上进行了评估，该数据集采集自低级别管状腺瘤患者，并根据后续是否发展为CRC分为病例组和对照组。XtraLight-MedMamba仅使用约32,000个参数，即达到了97.18%的准确率和0.9767的F1分数，其性能优于模型复杂度显著更高的基于Transformer的架构和传统Mamba架构。