用扩散模型生成热图数据，融合深度与手工特征提升乳腺癌检测准确率。
Breast Cancer Detection in Thermographic Images via Diffusion-Based Augmentation and Nonlinear Feature Fusion
- 用扩散概率模型生成热成像数据，解决医疗影像样本少的问题。
- 融合预训练ResNet-50与非线性特征，实现98.0%准确率和98.1%敏感度。
- 方法对可解释特征与生成数据的协同作用提供实证，适合医学图像研究者。
数据稀缺限制了深度学习在医学影像中的应用。本文提出一种基于扩散概率模型（DPM）的数据增强框架，用于热成像中的乳腺癌分类。实验表明，该DPM增强方法优于传统方法和ProGAN基线。框架将预训练ResNet-50的深层特征与从U-Net分割肿瘤中提取的手工非线性特征（如分形维数）进行融合，使用XGBoost分类器在融合特征上达到98.0%准确率和98.1%敏感度。消融实验与统计检验证实，DPM增强与非线性特征融合均是成功的关键且具有统计显著性。本工作验证了先进生成模型与可解释特征在构建高精度医疗诊断工具中的协同效应。
原文摘要 · Abstract (English)
Data scarcity hinders deep learning for medical imaging. We propose a framework for breast cancer classification in thermograms that addresses this using a Diffusion Probabilistic Model (DPM) for data augmentation. Our DPM-based augmentation is shown to be superior to both traditional methods and a ProGAN baseline. The framework fuses deep features from a pre-trained ResNet-50 with handcrafted nonlinear features (e.g., Fractal Dimension) derived from U-Net segmented tumors. An XGBoost classifier trained on these fused features achieves 98.0\% accuracy and 98.1\% sensitivity. Ablation studies and statistical tests confirm that both the DPM augmentation and the nonlinear feature fusion are critical, statistically significant components of this success. This work validates the synergy between advanced generative models and interpretable features for creating highly accurate medical diagnostic tools.
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