论文标题
使用图像熵的病理幻灯片进行资源苏制分类和分析
Resource-Frugal Classification and Analysis of Pathology Slides Using Image Entropy
论文作者
论文摘要
肺部恶性肿瘤的病理幻灯片是使用可能部署在移动设备上的资源 - 苏格拉尔卷积神经网络（CNN）进行分类的。特别是，分为两个阶段接近区分腺癌（LUAD）和鳞状细胞癌（LUSC）肺癌亚型的具有挑战性的任务。首先，将全扫描的组织病理学图像降采样至太大的CNN分析，但足够大，无法保留关键的解剖细节。倒下的图像被分解为较小的正方形瓷砖，这些瓷砖是根据其图像熵筛选的。轻巧的CNN产生瓷砖级分类，这些分类汇总以对幻灯片进行分类。所得精度可与具有更复杂的CNN和更大训练集获得的精度相媲美。为了允许临床医生在视觉上评估分类的基础 - 也就是说，要查看构成其构成的图像区域 - 颜色编码的概率图是通过重叠的瓷砖和平均瓷砖级别概率在像素级别平均创建的。
Pathology slides of lung malignancies are classified using resource-frugal convolution neural networks (CNNs) that may be deployed on mobile devices. In particular, the challenging task of distinguishing adenocarcinoma (LUAD) and squamous-cell carcinoma (LUSC) lung cancer subtypes is approached in two stages. First, whole-slide histopathology images are downsampled to a size too large for CNN analysis but large enough to retain key anatomic detail. The downsampled images are decomposed into smaller square tiles, which are sifted based on their image entropies. A lightweight CNN produces tile-level classifications that are aggregated to classify the slide. The resulting accuracies are comparable to those obtained with much more complex CNNs and larger training sets. To allow clinicians to visually assess the basis for the classification -- that is, to see the image regions that underlie it -- color-coded probability maps are created by overlapping tiles and averaging the tile-level probabilities at a pixel level.
