Semantic analysis of real endoscopies with unsupervised learned descriptorsDownload PDF

22 Apr 2022, 21:17 (edited 04 Jun 2022)MIDL 2022 Short PapersReaders: Everyone
  • Keywords: Endoscopy, unsupervised learning, image description, scene classification
  • TL;DR: We use representation learning to perform semantic video segmentation
  • Abstract: This work explores automatic analysis of medical procedure recordings, in particular, endoscopies. Regular medical practice recordings are noisy and challenging to process, so a quick and automatic overview of their content is essential. We show how advances in unsupervised representation learning can be applied to real medical data, obtaining rich descriptors to perform automatic semantic analysis of these recordings.
  • Registration: I acknowledge that acceptance of this work at MIDL requires at least one of the authors to register and present the work during the conference.
  • Authorship: I confirm that I am the author of this work and that it has not been submitted to another publication before.
  • Paper Type: novel methodological ideas without extensive validation
  • Primary Subject Area: Unsupervised Learning and Representation Learning
  • Secondary Subject Area: Application: Endoscopy
  • Confidentiality And Author Instructions: I read the call for papers and author instructions. I acknowledge that exceeding the page limit and/or altering the latex template can result in desk rejection.
1 Reply

Loading