论文标题
在H＆E上对H＆E染色的全片图像在肝细胞癌上的染色图像的多场分析
Analysis Of Multi Field Of View Cnn And Attention Cnn On H&E Stained Whole-slide Images On Hepatocellular Carcinoma
论文作者
论文摘要
肝细胞癌（HCC）是全球与癌症相关死亡的主要原因。全面的成像是扫描载玻片的一种方法，用于诊断HCC。对于卷积神经网络应用，使用高分辨率的全滑动图像是不可行的。因此，整个滑动图像是分配卷积神经网络进行分类和分割的常见方法。瓷砖大小的确定会影响算法的性能，因为较小的视野无法在更大的尺度上捕获信息，并且较大的视野无法在蜂窝尺度上捕获信息。在这项工作中，分析了瓷砖大小对分类问题性能的影响。此外，分配了多个视野CNN，以利用不同瓷砖大小提供的信息，并分配了注意CNN，以赋予投票最有贡献的瓷砖大小的能力。发现使用多个瓷砖大小可显着提高分类的性能3.97％，并且发现两种算法在仅使用一个瓷砖大小的算法上成功。
Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Whole-slide imaging which is a method of scanning glass slides have been employed for diagnosis of HCC. Using high resolution Whole-slide images is infeasible for Convolutional Neural Network applications. Hence tiling the Whole-slide images is a common methodology for assigning Convolutional Neural Networks for classification and segmentation. Determination of the tile size affects the performance of the algorithms since small field of view can not capture the information on a larger scale and large field of view can not capture the information on a cellular scale. In this work, the effect of tile size on performance for classification problem is analysed. In addition, Multi Field of View CNN is assigned for taking advantage of the information provided by different tile sizes and Attention CNN is assigned for giving the capability of voting most contributing tile size. It is found that employing more than one tile size significantly increases the performance of the classification by 3.97% and both algorithms are found successful over the algorithm which uses only one tile size.
