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 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 24 classes) from the GI tract. The dataset
comprises 8,000 images acquired from B{\ae}rum Hospital in Norway and
Karolinska University 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 this https URL

为了在临床实践中整合实时人工智能系统，需要解决数据可用性、数据质量和缺乏多样性数据集等挑战。为此，我们介绍了 GastroVision，一个多中心开放获取的胃肠内镜数据集，该数据集包括来自胃肠道的 24 种不同病理异常、息肉切除病例和正常发现，提供了大规模、精确标记的多样化数据集，并验证了数据集的重要性。我们相信，该数据集可以促进基于人工智能的胃肠疾病检测和分类算法的开发。