Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge

• URL: http://arxiv.org/abs/2507.04681v2
• Date: Sat, 12 Jul 2025 07:18:54 GMT
• Title: Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge
• Authors: Alper Bahcekapili, Duygu Arslan, Umut Ozdemir, Berkay Ozkirli, Emre Akbas, Ahmet Acar, Gozde B. Akar, Bingdou He, Shuoyu Xu, Umit Mert Caglar, Alptekin Temizel, Guillaume Picaud, Marc Chaumont, Gérard Subsol, Luc Téot, Fahad Alsharekh, Shahad Alghannam, Hexiang Mao, Wenhua Zhang,
• Abstract summary: Colorectal cancer (CRC) is the third most diagnosed cancer and the second leading cause of cancer-related death worldwide.<n>To promote automated and standardized solutions, we organized the ICIP Grand Challenge on Colorectal Cancer Tumor Grading.<n>The dataset comprises 103 whole-slide images with expert pixel-level annotations for five tissue classes.
• Score: 9.46817660446756
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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

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