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

ABSTRACT: 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.
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.
ERS: a novel comprehensive endoscopy image dataset for machine learning, compliant with the MST 3.0 specification
http://arxiv.org/abs/2201.08746v1