基于序列注意力的病理分析采样
Sequential Attention-based Sampling for Histopathological Analysis
AI总结：
SASHA通过基于序列注意力的深度强化学习方法，实现对高分辨率病理图像的高效分析，以较低的计算成本达到与现有方法相当的诊断性能。
摘要：
深度神经网络越来越多地应用于自动化病理学分析。然而，整个滑动图像（WSI）通常以十亿像素的尺寸获取，使在高分辨率下完全分析变得计算上不可行。诊断标签通常只在滑片级别可用，因为对图像进行更细粒度（块）级别的专家标注既费力又昂贵。此外，包含诊断信息的区域通常只占WSI的一小部分，使在全分辨率下检查整个滑片变得效率低下。本文提出了SASHA——基于序列注意力的病理分析采样——一种用于高效分析病理图像的深度强化学习方法。首先，SASHA通过轻量级的层次化、基于注意力的多实例学习（MIL）模型学习信息性特征。其次，SASHA能够智能地进行采样，并选择性地放大一小部分高分辨率块（10-20%）以实现可靠的诊断。我们证明，SASHA在高分辨率下完全分析WSI的性能与最先进的方法相当，尽管计算和内存成本仅为后者的几分之一。此外，它显著优于竞争的稀疏采样方法。我们提出SASHA作为医疗影像挑战中的智能采样模型，用于自动化诊断，这些挑战涉及包含稀疏信息特征的极大规模图像。模型实现可在：https://github.com/coglabiisc/SASHA上找到。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
