染色感知的小波正则化用于组织病理学中的即时对抗净化
Stain-Aware Wavelet Regularization for Instant Adversarial Purification in Histopathology
AI总结：
提出染色感知小波正则化（SAWR），利用Haar变换的多级小波域正则化分层分离对抗扰动与诊断结构信息，并扩展到组织学通道实现染色特异性频率调节，在即时净化框架中将对抗鲁棒性提升高达10.69%。
摘要：
深度学习在计算病理学流程中已变得普遍，支持癌症筛查和数字病理学分析等任务。然而，神经网络对对抗扰动的敏感性引发了临床实践中可靠部署的安全问题。在组织病理学图像中，由于难以区分高频对抗噪声与细微且具有诊断意义的组织结构，这一挑战更加严峻。为解决此问题，我们提出染色感知小波正则化（SAWR），一种利用基于Haar变换的多级小波域正则化的对抗净化框架，以分层方式将对抗扰动与诊断结构信息分离。该频谱约束进一步扩展到单个组织学通道，实现与苏木精和伊红的生物学特性一致的染色特异性频率调节。当集成到即时净化框架中时，SAWR将对抗鲁棒性相对于基线方法提升高达10.69%，同时在对抗扰动下保持纹理和频谱保真度。
英文摘要：
Deep learning has become prevalent in computational pathology pipelines that support tasks such as cancer screening and digital pathology analysis. However, the susceptibility of neural networks to adversarial perturbations raises safety concerns for reliable deployment in clinical practice. In histopathological images, this challenge is exacerbated by the difficulty of distinguishing high-frequency adversarial noise from subtle and diagnostically relevant tissue structures. To address this issue, we propose Stain-Aware Wavelet Regularization (SAWR), an adversarial purification framework that leverages multi-level wavelet-domain regularization based on Haar transform to hierarchically disentangle adversarial perturbations from diagnostic structural information. This spectral constraint is further extended to individual histological channels, enabling stain-specific frequency regulation consistent with the biological properties of Hematoxylin and Eosin. Extensive experiments demonstrate that SAWR improves adversarial robustness by up to 10.69\% over the baseline approach, while maintaining texture and spectral fidelity under adversarial perturbations.
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