Machine Learning in Endoscopy (EndoML)

This special session will focus on presentation of new and evaluation of existing AI (machine learning) techniques addressing key challenges in endoscopy with a focus on flexible and capsule endoscopy. The clinical application areas will include bronchoscopy, gastroscopy, colonoscopy, small bowel capsule endoscopy (SBCE), and colon capsule endoscopy (CCE). The challenges include raw data analysis, both unimodal and multimodal (e.g., fusing colonoscopy/CCE with CTC), novel visualisation techniques, and data mining incorporating clinical records for inference of disease epidemiology. The session will also welcome submissions on novel imaging techniques.

The special session is dedicated to the algorithmic, modelling, and computational aspects of [capsule] endoscopy with some focus on their clinical relevance. It aims to foster knowledge transfer between different scientific, clinical, and industrial communities.

We are looking for original, high-quality papers on the following topics of interest, but not limited to:

  • Computer-aided diagnosis in [capsule] endoscopy
  • Visualization in [capsule] endoscopy
  • Novel imaging techniques in [capsule] endoscopy
  • Detection and classification of pathology
  • Data mining for discovery of disease epidemiology
  • Segmentation/detection of organs and instruments in images/videos
  • Automatic assessment of bowel preparation
  • Workflow detection and analysis
  • Localisation, navigation, and 3D reconstruction in [capsule] endoscopy
  • Machine learning for [capsule] endoscopy image/video analysis
  • New datasets supporting the development of machine learning tools for [capsule] endoscopy
  • Tools for automation of capsule colonoscopy data analysis
  • Automatic read assistance in capsule endoscopy videos
  • Evaluation of intestinal motility
  • Clinical validation of the existing tools



Endoscopy, bronchoscopy, gastroscopy, enteroscopy, colonoscopy, flexible & capsule endoscopy lesions detection segmentation and classification, machine learning, computational automation, 3D reconstruction

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