XtraLight-MedMamba用于神经内分泌瘤分类
XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas
AI总结：
本文提出XtraLight-MedMamba模型，通过轻量级状态空间框架对全滑片图像中的神经内分泌瘤进行分类，利用ConvNeXt和Mamba块结合SCAB模块和FNOClassifier，实现高精度分类。
摘要：
准确的癌前息肉风险分层是减少结直肠癌（CRC）发病率的关键策略。然而，低级别不典型增生的评估仍受限于主观的组织病理学解释。计算病理学和深度学习的进步为识别与恶性转化相关的细微形态特征提供了新机会。本文提出XtraLight-MedMamba，一种基于状态空间的轻量级深度学习框架，用于从全滑片图像（WSIs）中分类神经内分泌瘤。该架构结合了基于ConvNeXt的浅层特征提取器和并行视觉Mamba块，以高效建模局部纹理线索和全局上下文结构。空间和通道注意力桥（SCAB）模块增强了多尺度特征提取，而固定非负正交分类器（FNOClassifier）实现了显著的参数减少和改进的泛化能力。模型在经过低级别管状腺瘤患者收集的定制数据集上进行了评估，该数据集根据后续CRC发展分为病例组和对照组。XtraLight-MedMamba在约32,000个参数的情况下实现了97.18%的准确率和0.9767的F1分数，优于基于Transformer和传统Mamba的架构，后者具有更高的模型复杂性和计算负担，使其适合资源受限的地区。
英文摘要：
Accurate risk stratification of precancerous polyps during routine colonoscopy screening is a key strategy to reduce the incidence of colorectal cancer (CRC). However, assessment of low-grade dysplasia remains limited by subjective histopathologic interpretation. Advances in computational pathology and deep learning offer new opportunities to identify subtle, 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 to classify neoplastic tubular adenomas from whole-slide images (WSIs). The architecture is a blend of a ConvNeXt-based shallow feature extractor with parallel vision mamba blocks to efficiently model local texture cues within global contextual structure. An integration of the Spatial and Channel Attention Bridge (SCAB) module enhances multiscale feature extraction, while the 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, which have significantly higher model complexity and computational burden, making it suitable for resource-constrained areas.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
