# GastroVision: A Multi-class Endoscopy Image Dataset for Computer Aided Gastrointestinal Disease Detection

arXiv: https://arxiv.org/abs/2307.08140 · HTML: https://mlsanity.com/paper/2307.08140/

## Key facts

- Cited by 20 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 26 references that are in the map.
- First posted on arXiv 16 Jul 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [Kvasir-SEG: A Segmented Polyp Dataset](https://mlsanity.com/paper/1911.07069/) (2019)
- [A multi-centre polyp detection and segmentation dataset for generalisability assessment](https://mlsanity.com/paper/2106.04463/) (2021)
- [Colonoscopy Polyp Detection and Classification: Dataset Creation and Comparative Evaluations](https://mlsanity.com/paper/2104.10824/) (2021)
- [Endoscopy disease detection challenge 2020](https://mlsanity.com/paper/2003.03376/) (2020)
- [The Medico-Task 2018: Disease Detection in the Gastrointestinal Tract using Global Features and Deep Learning](https://mlsanity.com/paper/1810.13278/) (2018)

## Built on by (most cited first, 20 of 20)

- [Frontiers in Intelligent Colonoscopy](https://mlsanity.com/paper/2410.17241/) (2024)
- [EndoBench: A Comprehensive Evaluation of Multi-Modal Large Language Models for Endoscopy Analysis](https://mlsanity.com/paper/2505.23601/) (2025)
- [Hallucination-Aware Multimodal Benchmark for Gastrointestinal Image Analysis with Large Vision-Language Models](https://mlsanity.com/paper/2505.07001/) (2025)
- [Classification of Endoscopy and Video Capsule Images using CNN-Transformer Model](https://mlsanity.com/paper/2408.10733/) (2024)
- [Domain-Adaptive Pre-training of Self-Supervised Foundation Models for Medical Image Classification in Gastrointestinal Endoscopy](https://mlsanity.com/paper/2410.21302/) (2024)
- [NCDD: Nearest Centroid Distance Deficit for Out-Of-Distribution Detection in Gastrointestinal Vision](https://mlsanity.com/paper/2412.01590/) (2024)
- [Diverse Image Generation with Diffusion Models and Cross Class Label Learning for Polyp Classification](https://mlsanity.com/paper/2502.05444/) (2025)
- [NERO: Explainable Out-of-Distribution Detection with Neuron-level Relevance](https://mlsanity.com/paper/2506.15404/) (2025)
- [EndoCogniAgent: Closed-Loop Agentic Reasoning with Self-Consistency Validation for Endoscopic Diagnosis](https://mlsanity.com/paper/2508.07292/) (2025)
- [MedicalNarratives: Connecting Medical Vision and Language with Localized Narratives](https://mlsanity.com/paper/2501.04184/) (2025)
- [Advancing AI-Powered Medical Image Synthesis: Insights from MedVQA-GI Challenge Using CLIP, Fine-Tuned Stable Diffusion, and Dream-Booth + LoRA](https://mlsanity.com/paper/2502.20667/) (2025)
- [Federated Foundation Model for GI Endoscopy Images](https://mlsanity.com/paper/2505.24108/) (2025)
- [A Medical Data-Effective Learning Benchmark for Highly Efficient Pre-training of Foundation Models](https://mlsanity.com/paper/2401.17542/) (2024)
- [Parameter Efficient Fine-tuning for Domain-specific Gastrointestinal Disease Recognition](https://mlsanity.com/paper/2604.10451/) (2026)
- [SAGE: An Expert-Annotated South Asian GI Endoscopy Dataset for Multimodal Learning and Hallucination Analysis](https://mlsanity.com/paper/2606.22144/) (2026)
- [EndoCIL: A Class-Incremental Learning Framework for Endoscopic Image Classification](https://mlsanity.com/paper/2510.17200/) (2025)
- [Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis](https://mlsanity.com/paper/2604.05649/) (2026)
- [DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection](https://mlsanity.com/paper/2604.08261/) (2026)
- [woma: a real-time foundation model and its fine-tuned models for endoscopy](https://mlsanity.com/paper/2609.15130/) (2026)
- [Generalized Biomedicine Discovery](https://mlsanity.com/paper/2610.00120/) (2026)

## Abstract (arXiv)

Integrating real-time artificial intelligence (AI) systems in clinical practices faces challenges such as scalability and acceptance. These challenges include data availability, biased outcomes, data quality, lack of transparency, and underperformance on unseen datasets from different distributions. The scarcity of large-scale, precisely labeled, and diverse datasets are the major challenge for clinical integration. This scarcity is also due to the legal restrictions and extensive manual efforts required for accurate annotations from clinicians. To address these challenges, we present textitGastroVision, a multi-center open-access gastrointestinal (GI) endoscopy dataset that includes different anatomical landmarks, pathological abnormalities, polyp removal cases and normal findings (a total of 27 classes) from the GI tract. The dataset comprises 8,000 images acquired from Baerum Hospital in Norway and Karolinska University Hospital in Sweden and was annotated and verified by experienced GI endoscopists. Furthermore, we validate the significance of our dataset with extensive benchmarking based on the popular deep learning based baseline models. We believe our dataset can facilitate the development of AI-based algorithms for GI disease detection and classification. Our dataset is available at urlhttps://osf.io/84e7f/.