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Colorectal cancer tumor grade segmentation in digital histopathology images: From giga to mini challenge.
In: (2025 IEEE International Conference on Image Processing Workshops, ICIPW 2025, 14-17 September 2025, Anchorage). 2025. 48-53 (2025 IEEE International Conference on Image Processing Workshops Icipw 2025 Proceedings)
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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Publication type
Article: Conference contribution
Keywords
Colorectal Cancer ; Digital Histopathology ; Tumor Grade Segmentation
ISSN (print) / ISBN
[9798331577995]
Conference Title
2025 IEEE International Conference on Image Processing Workshops, ICIPW 2025
Conference Date
14-17 September 2025
Conference Location
Anchorage
Quellenangaben
Pages: 48-53
Institute(s)
Institute of Environmental Medicine (IEM)