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

TL;DR

GastroVision is a large, diverse, and well-annotated endoscopy image dataset designed to advance AI-based gastrointestinal disease detection and classification, addressing key challenges in clinical AI integration.

Contribution

We introduce GastroVision, a comprehensive multi-center GI endoscopy dataset with extensive annotations, enabling improved AI development and benchmarking for GI disease detection.

Findings

Dataset includes 8,000 images from two hospitals.

Extensive benchmarking validates dataset's utility.

Annotations verified by expert GI endoscopists.

Abstract

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 \textit{GastroVision}, 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 B{\ae}rum 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 \url{https://osf.io/84e7f/}.