  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.
