用信号处理框架从脑组织X射线数据中精准识别阿尔茨海默病特征结构。
A Multicollinearity-Aware Signal-Processing Framework for Cross-$β$ Identification via X-ray Scattering of Alzheimer's Tissue
- 分三阶段处理：去噪、降冗余、分类，应对高维相关数据挑战。
- 仅用11个特征和174参数就达到84.3%的F1分数，效果出色。
- 适合做神经退行性疾病病理分析的科研人员或医学影像研究者。
对死后人脑组织进行原位X射线散射测量可捕捉到阿尔茨海默病标志性跨β折叠结构的形貌特征，但因基底污染、强特征相关性及样本量有限，自动化检测仍具挑战。本文提出一种三阶段分类框架：第一阶段采用贝叶斯最优分类器区分云母基底与组织区域；第二阶段引入多共线性感知的类别条件相关性剪枝策略，在保证贝叶斯风险与近似误差的前提下减少冗余，保留判别信息；第三阶段在剪枝后的特征集上训练轻量级神经网络，以检测跨β纤维有序结构的存在与否。最佳模型通过结合焦点损失与Dice损失优化，使用211个候选特征中的11个及174个可训练参数，测试F1得分为84.30%。整体框架提供了一种适用于高维、相关性强、数据受限实验测量的可解释性、理论驱动的分类策略，本研究以神经退行性组织的X射线散射图为例。
原文摘要 · Abstract (English)
X-ray scattering measurements of in situ human brain tissue encode structural signatures of pathological cross-$β$ inclusions, yet systematic exploitation of these data for automated detection remains challenging due to substrate contamination, strong inter-feature correlations, and limited sample sizes. This work develops a three-stage classification framework for identifying cross-$β$ structural inclusions-a hallmark of Alzheimer's disease-in X-ray scattering profiles of post-mortem human brain. Stage 1 employs a Bayes-optimal classifier to separate mica substrate from tissue regions on the basis of their distinct scattering signatures. Stage 2 introduces a multicollinearityaware, class-conditional correlation pruning scheme with formal guarantees on the induced Bayes risk and approximation error, thereby reducing redundancy while retaining class-discriminative information. Stage 3 trains a compact neural network on the pruned feature set to detect the presence or absence of cross-$β$ fibrillar ordering. The top-performing model, optimized with a composite loss combining Focal and Dice objectives, attains a test F1-score of 84.30% using 11 of 211 candidate features and 174 trainable parameters. The overall framework yields an interpretable, theory-grounded strategy for data-limited classification problems involving correlated, high-dimensional experimental measurements, exemplified here by X-ray scattering profiles of neurodegenerative tissue.
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