用小波分析选关键组织区域，提升病理图像自监督学习效果
Learning from the Right Patches: A Two-Stage Wavelet-Driven Masked Autoencoder for Histopathology Representation Learning
- 先低倍小波筛选结构丰富区，再高倍提取细节，模拟医生诊断流程
- 在肺癌、肾癌、结直肠癌数据集上分类准确率优于传统方法
- 轻量高效，适合标注稀缺的病理图像学习，尤其适合医学影像研究者
全切片图像是数字病理的核心，但其尺寸巨大且标注稀少，自监督学习至关重要。基于视觉变换器的掩码自编码器（MAE）在病理表示学习中展现出强大潜力。然而，传统MAE预训练中的随机图像块采样常包含无关或噪声区域，限制了模型对有意义组织模式的捕捉能力。本文提出一种轻量级、领域适配的框架——WISE-MAE，通过小波引导的图像块选择策略，将结构与生物学相关性引入基于MAE的学习过程。该方法采用两阶段粗到细的流程：在低倍率下利用小波分析筛选结构丰富的区域，再在高分辨率下提取用于详细建模。这一策略模仿病理学家的诊断流程，提升了学习表示的质量。在多个癌症数据集（包括肺、肾和结直肠组织）上的评估表明，WISE-MAE在弱监督条件下实现了具有竞争力的表示质量与下游分类性能，同时保持高效。
原文摘要 · Abstract (English)
Whole-slide images are central to digital pathology, yet their extreme size and scarce annotations make self-supervised learning essential. Masked Autoencoders (MAEs) with Vision Transformer backbones have recently shown strong potential for histopathology representation learning. However, conventional random patch sampling during MAE pretraining often includes irrelevant or noisy regions, limiting the model's ability to capture meaningful tissue patterns. In this paper, we present a lightweight and domain-adapted framework that brings structure and biological relevance into MAE-based learning through a wavelet-informed patch selection strategy. WISE-MAE applies a two-step coarse-to-fine process: wavelet-based screening at low magnification to locate structurally rich regions, followed by high-resolution extraction for detailed modeling. This approach mirrors the diagnostic workflow of pathologists and improves the quality of learned representations. Evaluations across multiple cancer datasets, including lung, renal, and colorectal tissues, show that WISE-MAE achieves competitive representation quality and downstream classification performance while maintaining efficiency under weak supervision.
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