Patch-Based Cervical Cancer Segmentation using Distance from Boundary of Tissue
This work addresses the automation of pathological diagnosis for cervical cancer segmentation, but it is incremental as it builds on existing patch-based methods with a specific enhancement.
The paper tackled the problem of segmenting cervical cancer in Whole Slide Images by using a patch-based approach enhanced with Distance from the Boundary of tissue (DfB) to incorporate global information, resulting in improved total performance compared to conventional methods.
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.