Colorectal Cancer Tumor Grade Segmentation in Digital Histopathology Images: From Giga to Mini Challenge
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Abstract (arXiv)
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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References · 11
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- 2016Overcoming catastrophic forgetting in neural networkscited by 4,787
- 2019Towards Robust Monocular Depth Estimation: Mixing Datasets for Zero-shot Cross-dataset Transfercited by 1,495
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- 2022Vision Transformer Adapter for Dense Predictionscited by 565
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- 2022Visual Attention Networkcited by 210
- 2019HarDNet: A Low Memory Traffic Networkcited by 76
- 2022Lawin Transformer: Improving Semantic Segmentation Transformer with Multi-Scale Representations via Large Window Attentioncited by 30
- 2024NCT-CRC-HE: Not All Histopathological Datasets Are Equally Usefulcited by 5
- 2024Adaptive Augmentation Policy Optimization with LLM Feedbackcited by 2
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