The early detection of breast abnormalities is a matter of critical significance. Notably, infrared thermography has emerged as a valuable tool in breast cancer screening and clinical breast examination (CBE). Measuring heterogeneous thermal patterns is the key to incorporating computational dynamic thermography, which can be achieved by matrix factorization techniques. These approaches focus on extracting the predominant thermal patterns from the entire thermal sequence. Yet, the task of singling out the dominant image that effectively represents the prevailing temporal changes remains a challenging pursuit within the field of computational thermography. In this context, we propose applying James-Stein for eigenvector (JSE) and Weibull embedding approaches, as two novel strategies in response to this challenge. The primary objective is to create a low-dimensional (LD) representation of the thermal data stream. This LD approximation serves as the foundation for extracting thermomics and training a classification model with optimized hyperparameters, for early breast cancer detection. Furthermore, we conduct a comparative analysis of various embedding adjuncts to matrix factorization methods. The results of the proposed method indicate an enhancement in the projection of the predominant basis vector, yielding classification accuracy of 81.7% (+/-5.2%) using Weibull embedding, which outperformed other embedding approaches we proposed previously. In comparison analysis, Sparse PCT and Deep SemiNMF showed the highest accuracies having 80.9% and 78.6%, respectively. These findings suggest that JSE and Weibull embedding techniques substantially help preserve crucial thermal patterns as a biomarker leading to improved CBE and enabling the very early detection of breast cancer.

翻译：乳腺异常的早期检测具有关键临床意义。值得注意的是，红外热成像已成为乳腺癌筛查与临床乳腺检查（CBE）中的重要工具。测量异质性热模式是实现计算动态热成像的关键，这可通过矩阵分解技术实现。此类方法致力于从完整热序列中提取主导热模式。然而，如何有效提取能够表征主导时序变化的代表性图像，仍是计算热成像领域面临的挑战性课题。针对此问题，我们提出应用詹姆斯-斯坦特征向量（JSE）与威布尔嵌入两种创新策略。其核心目标是构建热数据流的低维（LD）表示，该低维近似可作为提取热组学特征的基础，并用于训练具有优化超参数的分类模型以实现早期乳腺癌检测。此外，我们系统比较了矩阵分解方法中多种嵌入辅助技术的性能。实验结果表明，所提方法能有效增强主导基向量的投影质量，其中威布尔嵌入方法实现了81.7%（±5.2%）的分类准确率，优于我们先前提出的其他嵌入方案。对比分析显示，稀疏主成分变换（Sparse PCT）与深度半非负矩阵分解（Deep SemiNMF）分别获得80.9%和78.6%的最高准确率。这些发现表明，JSE与威布尔嵌入技术能有效保留关键热模式作为生物标志物，从而提升临床乳腺检查效果，实现乳腺癌的极早期检测。