COLORECTAL CANCER TUMOR GRADE SEGMENTATION IN DIGITAL HISTOPATHOLOGY IMAGES: FROM GIGA TO MINI CHALLENGE

Date

2025-01-01

Author

Bahcekapili, Alper
Arslan, Duygu
Ozdemir, Umut
Ozkirli, Berkay
Akbaş, Emre
Acar, Ahmet
Akar, Gözde
He, Bingdou
Xu, Shuoyu
Çağlar, Ümit Mert
Temizel, Alptekin
Picaud, Guillaume
Chaumont, Marc
Subsol, Gerard
Teot, Luc
Alsharekh, Fahad
Alghannam, Shahad
Mao, Hexiang
Zhang, Wenhua

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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.

URI

https://hdl.handle.net/11511/119499

Collections

Department of Computer Engineering, Conference / Seminar