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.

翻译：原位人脑组织的X射线散射测量编码了病理性交叉β包涵体的结构特征，但由于基底污染、特征间强相关性以及样本量有限，系统利用这些数据进行自动化检测仍面临挑战。本研究开发了一个三阶段分类框架，用于在死后人脑组织的X射线散射谱中识别交叉β结构包涵体——阿尔茨海默症的标志性特征。第一阶段采用贝叶斯最优分类器，依据云母基底与组织区域不同的散射特征进行分离。第二阶段引入一种多重共线性感知的类条件相关性剪枝方案，该方案对诱导的贝叶斯风险与近似误差提供形式化保证，从而在保留类别判别信息的同时减少冗余。第三阶段在剪枝后的特征集上训练一个紧凑的神经网络，以检测交叉β纤维状有序结构的存在与否。通过结合Focal损失与Dice损失的复合损失函数优化，最佳模型仅使用211个候选特征中的11个特征和174个可训练参数，在测试集上取得了84.30%的F1分数。该整体框架为涉及高维相关实验测量（此处以神经退行性组织X射线散射谱为例）且数据有限的分类问题，提供了一种可解释的、理论依据充分的策略。