ArticleFrontiers in medicine2023
EBHI-Seg: A novel enteroscope biopsy histopathological hematoxylin and eosin image dataset for image segmentation tasks.
Article in Frontiers in medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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9 citing papers in PubMed.
- Artificial Intelligence-Based Histopathology Segmentation for Resource-Constrained Healthcare Systems.Diagnostics (Basel, Switzerland) · 2026Article
- Enhanced colorectal gland segmentation through multi-scale attention and contextual feature fusion.Scientific reports · 2026Article
- MAP-SCTNet: multi-scale pyramid and frequency-enhanced network for colorectal cancer histopathological image segmentation.Frontiers in medicine · 2026Article
- Colorectal Lesion diagnosis using transformer and deep learning with multiscale feature interface.PloS one · 2026Article
- Deep multimodal fusion of patho-radiomic and clinical data for enhanced survival prediction for colorectal cancer patients.NPJ digital medicine · 2025Article
- A hybrid framework for enhanced segmentation and classification of colorectal cancer histopathology.Frontiers in artificial intelligence · 2025Article
- Deep learning-based classification of colorectal cancer in histopathology images for category detection.Biology methods & protocols · 2025Article
- Optimizing colorectal cancer segmentation with MobileViT-UNet and multi-criteria decision analysis.PeerJ. Computer science · 2024Article
- Computer-aided colorectal cancer diagnosis: AI-driven image segmentation and classification.PeerJ. Computer science · 2024Article
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18 authors.
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Abstract
Background and purpose: Colorectal cancer is a common fatal malignancy, the fourth most common cancer in men, and the third most common cancer in women worldwide. Timely detection of cancer in its early stages is essential for treating the disease. Currently, there is a lack of datasets for histopathological image segmentation of colorectal cancer, which often hampers the assessment accuracy when computer technology is used to aid in diagnosis. Methods: This present study provided a new publicly available Results: The experimental results showed that deep learning methods had a better image segmentation performance when utilizing EBHI-Seg. The maximum accuracy of the Dice evaluation metric for the classical machine learning method is 0.948, while the Dice evaluation metric for the deep learning method is 0.965. Conclusion: This publicly available dataset contained 4,456 images of six types of tumor differentiation stages and the corresponding ground truth images. The dataset can provide researchers with new segmentation algorithms for medical diagnosis of colorectal cancer, which can be used in the clinical setting to help doctors and patients. EBHI-Seg is publicly available at: https://figshare.com/articles/dataset/EBHI-SEG/21540159/1.
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