Deep neural networks are increasingly applied in automated histopathology. Yet, whole-slide images (WSIs) are often acquired at gigapixel sizes, rendering them computationally infeasible to analyze entirely at high resolution. Diagnostic labels are largely available only at the slide-level, because expert annotation of images at a finer (patch) level is both laborious and expensive. Moreover, regions with diagnostic information typically occupy only a small fraction of the WSI, making it inefficient to examine the entire slide at full resolution. Here, we propose SASHA -- Sequential Attention-based Sampling for Histopathological Analysis -- a deep reinforcement learning approach for efficient analysis of histopathological images. First, SASHA learns informative features with a lightweight hierarchical, attention-based multiple instance learning (MIL) model. Second, SASHA samples intelligently and zooms selectively into a small fraction (10-20\%) of high-resolution patches to achieve reliable diagnoses. We show that SASHA matches state-of-the-art methods that analyze the WSI fully at high resolution, albeit at a fraction of their computational and memory costs. In addition, it significantly outperforms competing, sparse sampling methods. We propose SASHA as an intelligent sampling model for medical imaging challenges that involve automated diagnosis with exceptionally large images containing sparsely informative features. Model implementation is available at: https://github.com/coglabiisc/SASHA.

翻译：深度神经网络在自动化组织病理学中的应用日益广泛。然而，全切片图像通常以十亿像素级尺寸获取，使得在高分辨率下完整分析在计算上不可行。诊断标签大多仅在切片级别可用，因为在更精细（区块）级别对图像进行专家标注既费力又昂贵。此外，具有诊断信息的区域通常仅占全切片图像的极小部分，因此以全分辨率检查整个切片效率低下。本文提出SASHA——一种用于组织病理学图像高效分析的深度强化学习方法。首先，SASHA通过轻量级分层注意力多示例学习模型学习信息特征。其次，SASHA智能采样并选择性地放大至高分辨率区块的小部分（10-20%）以实现可靠诊断。我们证明，SASHA在仅需部分计算和内存成本的情况下，即可达到对全切片图像进行完整高分辨率分析的最新方法性能。此外，其显著优于其他稀疏采样方法。我们提出将SASHA作为医学影像挑战的智能采样模型，适用于包含稀疏信息特征的超大型图像的自动化诊断任务。模型实现发布于：https://github.com/coglabiisc/SASHA。