论文标题
ERS：一种用于机器学习的新型综合内窥镜图像数据集，符合MST 3.0规范
ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specification
论文作者
论文摘要
本文提供了一个新的多标签综合图像数据集，该数据集来自灵活的内窥镜检查，结肠镜检查和胶囊内窥镜检查，名为ERS。该系列已根据“最低标准术语3.0”（MST 3.0）的完整医学规范进行了标记，描述了胃肠道（104个可能的标签）中所有可能的发现，并在共同的机器学习应用中使用了其他19个标签。 该数据集可精确地包含大约6000个，其中115,000个来自内窥镜视频，3600个精确和22,600个大约分段掩码的框架，以及来自灵活和胶囊内窥镜视频的123万个未标记的框架。标记的数据几乎完全涵盖了MST 3.0标准。数据来自1135例患者的1520个视频。 此外，本文提出并描述了使用创建数据集执行的胃肠道图像分类任务中的四个示例性实验。获得的结果表明，在内窥镜数据分析领域，数据集在培训和测试机学习算法中的高实用性和灵活性。
The article presents a new multi-label comprehensive image dataset from flexible endoscopy, colonoscopy and capsule endoscopy, named ERS. The collection has been labeled according to the full medical specification of 'Minimum Standard Terminology 3.0' (MST 3.0), describing all possible findings in the gastrointestinal tract (104 possible labels), extended with an additional 19 labels useful in common machine learning applications. The dataset contains around 6000 precisely and 115,000 approximately labeled frames from endoscopy videos, 3600 precise and 22,600 approximate segmentation masks, and 1.23 million unlabeled frames from flexible and capsule endoscopy videos. The labeled data cover almost entirely the MST 3.0 standard. The data came from 1520 videos of 1135 patients. Additionally, this paper proposes and describes four exemplary experiments in gastrointestinal image classification task performed using the created dataset. The obtained results indicate the high usefulness and flexibility of the dataset in training and testing machine learning algorithms in the field of endoscopic data analysis.
