论文标题
在癌症病理学的大型多尺度图像中选择感兴趣的区域
Selecting Regions of Interest in Large Multi-Scale Images for Cancer Pathology
论文作者
论文摘要
使用卷积神经网络（CNN）在对象检测和图像分类方面的最新突破正在彻底改变医学成像中的最新状态，特别是显微镜为计算机视觉算法提供了丰富的机会，可以帮助医学专业人员诊断从疟疾到癌症，以帮助医学专业人员诊断。基于多个尺度和分辨率的幻灯片图像中特征的测量值，对癌症病理学家的高分辨率扫描（WSIS）为癌症病理学家提供了足够的信息，可以使癌症病理学家得出有关癌症的存在，亚型和严重性的结论。 WSIS的极高分辨率和特征量表，从总的解剖结构到细胞核，排除了标准CNN模型用于对象检测和分类的使用，通常是为数百个像素的图像而设计的，并且具有图像本身大小的对象。我们探索基于强化学习和梁搜索的平行方法，以学习逐渐放大WSI，以检测包含两种类型的肝癌之一，即肝细胞癌（HCC）和胆管癌（CC）的肝脏病理幻灯片中的感兴趣区域（ROI）。然后可以将这些ROI直接呈现给病理学家，以帮助测量和诊断，或用于肿瘤亚型的自动分类。
Recent breakthroughs in object detection and image classification using Convolutional Neural Networks (CNNs) are revolutionizing the state of the art in medical imaging, and microscopy in particular presents abundant opportunities for computer vision algorithms to assist medical professionals in diagnosis of diseases ranging from malaria to cancer. High resolution scans of microscopy slides called Whole Slide Images (WSIs) offer enough information for a cancer pathologist to come to a conclusion regarding cancer presence, subtype, and severity based on measurements of features within the slide image at multiple scales and resolutions. WSIs' extremely high resolutions and feature scales ranging from gross anatomical structures down to cell nuclei preclude the use of standard CNN models for object detection and classification, which have typically been designed for images with dimensions in the hundreds of pixels and with objects on the order of the size of the image itself. We explore parallel approaches based on Reinforcement Learning and Beam Search to learn to progressively zoom into the WSI to detect Regions of Interest (ROIs) in liver pathology slides containing one of two types of liver cancer, namely Hepatocellular Carcinoma (HCC) and Cholangiocarcinoma (CC). These ROIs can then be presented directly to the pathologist to aid in measurement and diagnosis or be used for automated classification of tumor subtype.
