Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge
Journal:
arXiv
Published Date:
Jul 7, 2025
Abstract
Colorectal cancer (CRC) is the third most diagnosed cancer and the second
leading cause of cancer-related death worldwide. Accurate histopathological
grading of CRC is essential for prognosis and treatment planning but remains a
subjective process prone to observer variability and limited by global
shortages of trained pathologists. To promote automated and standardized
solutions, we organized the ICIP Grand Challenge on Colorectal Cancer Tumor
Grading and Segmentation using the publicly available METU CCTGS dataset. The
dataset comprises 103 whole-slide images with expert pixel-level annotations
for five tissue classes. Participants submitted segmentation masks via Codalab,
evaluated using metrics such as macro F-score and mIoU. Among 39 participating
teams, six outperformed the Swin Transformer baseline (62.92 F-score). This
paper presents an overview of the challenge, dataset, and the top-performing
methods
