Pathological diagnosis is used for examining cancer in detail, and its automation is in demand. To automatically segment each cancer area, a patch-based approach is usually used since a Whole Slide Image (WSI) is huge. However, this approach loses the global information needed to distinguish between classes. In this paper, we utilized the Distance from the Boundary of tissue (DfB), which is global information that can be extracted from the original image. We experimentally applied our method to the three-class classification of cervical cancer, and found that it improved the total performance compared with the conventional method.

翻译：病理诊断用于详细检查癌症,需要自动化。对于每个癌症领域的自动分块,通常使用补丁法,因为全幻灯片图象(WSI)是巨大的。然而,这种方法失去了区分不同类别所需的全球信息。在本文中,我们使用了从组织界限的距离(DfB),这是可以从原始图象中提取的全球信息。我们实验地将我们的方法应用于子宫颈癌的三类分类,发现它比常规法提高了总体性能。