数字病理学中可控潜空间增强
Controllable Latent Space Augmentation for Digital Pathology
AI总结：
针对数字病理学中WSI分析的高分辨率、监督信号稀缺及现有增强方法的不足，提出可控潜空间增强模型HistAug，通过显式patch变换条件生成逼真嵌入，高效处理大量patch并提升MIL性能，低数据场景下优于现有方法。
摘要：
数字病理学中的全切片图像（WSI）分析面临独特挑战，原因在于WSI的十亿像素分辨率以及密集监督信号的稀缺性。虽然多实例学习（MIL）天然适用于切片级任务，但训练鲁棒模型需要大型且多样化的数据集。尽管图像增强技术可用于增加数据变异性并减少过拟合，但有效实现这些技术并非易事。传统的 patch 级增强由于每个 WSI 提取的 patch 数量庞大而成本过高，且现有特征级增强方法缺乏对变换语义的控制。我们提出 HistAug，这是一种快速高效的生成模型，用于数字病理学潜空间中的可控增强。通过以显式的 patch 级变换（如色调、腐蚀）为条件，HistAug 在保留初始语义信息的同时生成逼真的增强嵌入。我们的方法能够在单次前向传递中高效处理大量 patch，同时持续提升 MIL 模型性能。在多个切片级任务和不同器官上的实验表明，HistAug 优于现有方法，尤其在低数据场景下表现突出。消融研究证实了学习变换相较于基于噪声的扰动的优势，并强调了统一的 WSI 级增强的重要性。代码可在 https://github.com/MICS-Lab/HistAug 获取。
英文摘要：
Whole slide image (WSI) analysis in digital pathology presents unique challenges due to the gigapixel resolution of WSIs and the scarcity of dense supervision signals. While Multiple Instance Learning (MIL) is a natural fit for slide-level tasks, training robust models requires large and diverse datasets. Even though image augmentation techniques could be utilized to increase data variability and reduce overfitting, implementing them effectively is not a trivial task. Traditional patch-level augmentation is prohibitively expensive due to the large number of patches extracted from each WSI, and existing feature-level augmentation methods lack control over transformation semantics. We introduce HistAug, a fast and efficient generative model for controllable augmentations in the latent space for digital pathology. By conditioning on explicit patch-level transformations (e.g., hue, erosion), HistAug generates realistic augmented embeddings while preserving initial semantic information. Our method allows the processing of a large number of patches in a single forward pass efficiently, while at the same time consistently improving MIL model performance. Experiments across multiple slide-level tasks and diverse organs show that HistAug outperforms existing methods, particularly in low-data regimes. Ablation studies confirm the benefits of learned transformations over noise-based perturbations and highlight the importance of uniform WSI-wise augmentation. Code is available at https://github.com/MICS-Lab/HistAug.
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