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The article presents a new multi-label comprehensive image dataset fromflexible endoscopy, colonoscopy and capsule endoscopy, named ERS. Thecollection has been labeled according to the full medical specification of'Minimum Standard Terminology 3.0' (MST 3.0), describing all possible findingsin the gastrointestinal tract (104 possible labels), extended with anadditional 19 labels useful in common machine learning applications.  The dataset contains around 6000 precisely and 115,000 approximately labeledframes from endoscopy videos, 3600 precise and 22,600 approximate segmentationmasks, and 1.23 million unlabeled frames from flexible and capsule endoscopyvideos. The labeled data cover almost entirely the MST 3.0 standard. The datacame from 1520 videos of 1135 patients.  Additionally, this paper proposes and describes four exemplary experiments ingastrointestinal image classification task performed using the created dataset.The obtained results indicate the high usefulness and flexibility of thedataset in training and testing machine learning algorithms in the field ofendoscopic data analysis.

ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specification