# EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks

arXiv: https://arxiv.org/abs/2212.00532 · HTML: https://mlsanity.com/paper/2212.00532/

## Key facts

- Cited by 15 papers in the map (data through 14 Oct 2026).
- Builds on 22 of its 70 references that are in the map.
- First posted on arXiv 1 Dec 2022.

## Builds on (most cited first, 22 of 22)

- [SegNet: A Deep Convolutional Encoder-Decoder Architecture for Image Segmentation](https://mlsanity.com/paper/1511.00561/) (2015)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [Learn like a Pathologist: Curriculum Learning by Annotator Agreement for Histopathology Image Classification](https://mlsanity.com/paper/2009.13698/) (2020)
- [GasHis-Transformer: A Multi-scale Visual Transformer Approach for Gastric Histopathological Image Detection](https://mlsanity.com/paper/2104.14528/) (2021)
- [DeepCervix: A Deep Learning-based Framework for the Classification of Cervical Cells Using Hybrid Deep Feature Fusion Techniques](https://mlsanity.com/paper/2102.12191/) (2021)
- [A Comprehensive Review of Image Analysis Methods for Microorganism Counting: From Classical Image Processing to Deep Learning Approaches](https://mlsanity.com/paper/2103.13625/) (2021)
- [GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer](https://mlsanity.com/paper/2106.02473/) (2021)
- [Gastric histopathology image segmentation using a hierarchical conditional random field](https://mlsanity.com/paper/2003.01302/) (2020)
- [Is the aspect ratio of cells important in deep learning? A robust comparison of deep learning methods for multi-scale cytopathology cell image classification: from convolutional neural networks to visual transformers](https://mlsanity.com/paper/2105.07402/) (2021)
- [Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer](https://mlsanity.com/paper/2108.00358/) (2021)
- [A Comprehensive Review for MRF and CRF Approaches in Pathology Image Analysis](https://mlsanity.com/paper/2009.13721/) (2020)
- [A New Pairwise Deep Learning Feature For Environmental Microorganism Image Analysis](https://mlsanity.com/paper/2102.12147/) (2021)
- [A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches](https://mlsanity.com/paper/2105.03148/) (2021)
- [A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification](https://mlsanity.com/paper/2102.10499/) (2021)
- [A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers](https://mlsanity.com/paper/2107.07699/) (2021)
- [CVM-Cervix: A Hybrid Cervical Pap-Smear Image Classification Framework Using CNN, Visual Transformer and Multilayer Perceptron](https://mlsanity.com/paper/2206.00971/) (2022)
- [IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach](https://mlsanity.com/paper/2206.03368/) (2022)
- [TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos](https://mlsanity.com/paper/2204.08166/) (2022)
- [A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer](https://mlsanity.com/paper/2205.12843/) (2022)
- [A Comprehensive Survey with Quantitative Comparison of Image Analysis Methods for Microorganism Biovolume Measurements](https://mlsanity.com/paper/2202.09020/) (2022)
- [Segmentation of Weakly Visible Environmental Microorganism Images Using Pair-wise Deep Learning Features](https://mlsanity.com/paper/2208.14957/) (2022)

## Built on by (most cited first, 15 of 15)

- [Lingshu: A Generalist Foundation Model for Unified Multimodal Medical Understanding and Reasoning](https://mlsanity.com/paper/2506.07044/) (2025)
- [SAM2 for Image and Video Segmentation: A Comprehensive Survey](https://mlsanity.com/paper/2503.12781/) (2025)
- [TP-Seg: Task-Prototype Framework for Unified Medical Lesion Segmentation](https://mlsanity.com/paper/2604.00684/) (2026)
- [Path-SAM2: Transfer SAM2 for digital pathology semantic segmentation](https://mlsanity.com/paper/2408.03651/) (2024)
- [To pretrain or not to pretrain? A case study of domain-specific pretraining for semantic segmentation in histopathology](https://mlsanity.com/paper/2307.03275/) (2023)
- [UniSegDiff: Boosting Unified Lesion Segmentation via a Staged Diffusion Model](https://mlsanity.com/paper/2507.18362/) (2025)
- [Continual Alignment for SAM: Rethinking Foundation Models for Medical Image Segmentation in Continual Learning](https://mlsanity.com/paper/2511.17201/) (2025)
- [SAGE: Shape-Adapting Gated Experts for Adaptive Histopathology Image Segmentation](https://mlsanity.com/paper/2511.18493/) (2025)
- [MARVEL: Margin-Aware Robust von Mises-Fischer Expert Learning for Long-Tailed Out-of-Distribution Detection](https://mlsanity.com/paper/2607.02435/) (2026)
- [SPEEDNet: Salient Pyramidal Enhancement Encoder-Decoder Network for Colonoscopy Images](https://mlsanity.com/paper/2312.01128/) (2023)
- [R-Net: A Reliable and Resource-Efficient CNN for Colorectal Cancer Detection with XAI Integration](https://mlsanity.com/paper/2509.16251/) (2025)
- [Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation](https://mlsanity.com/paper/2603.17746/) (2026)
- [Performance and Interpretability of Convolutional, Transformer, and Hybrid Deep Learning Models in Colorectal Histology Classification](https://mlsanity.com/paper/2606.23744/) (2026)
- [Slide-Level Active Learning Reduces Annotation Burden in H&E images](https://mlsanity.com/paper/2607.09831/) (2026)
- [Colorectal Cancer Segmentation with Adaptive Augmentation and Multi-Resolution Ensemble Models](https://mlsanity.com/paper/2609.38419/) (2026)

## Abstract (arXiv)

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 rectal 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 Enteroscope Biopsy Histopathological Hematoxylin and Eosin Image Dataset for Image Segmentation Tasks (EBHI-Seg). To demonstrate the validity and extensiveness of EBHI-Seg, the experimental results for EBHI-Seg are evaluated using classical machine learning methods and deep learning methods. 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 5,170 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.