Evaluating Self-Supervised Learning Methods for Downstream Classification of Neoplasia in Barrett’s Esophagus

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

Abstract

A major problem in applying machine learning for the medical domain is the scarcity of labeled data, which results in the demand for methods that enable high-quality models trained with little to no labels. Self-supervised learning methods present a plausible solution to this problem, enabling the use of large sets of unlabeled data for model pretraining. In this study, multiple of these methods and training strategies are employed on a large dataset of endoscopic images from the gastrointestinal tract (GastroNet). The suitability of these methods is assessed for an intra-domain downstream classification task on a small endoscopic dataset, involving neoplasia in Barrett’s esophagus. The classification performances are compared against pretraining on ImageNet and training from scratch. This yields promising results for domain-specific self-supervised methods, where super-resolution outperforms pretraining on ImageNet with a mean classification accuracy of 83.8% (cf. 79.2%). This implies that the large amounts of unlabeled data in hospitals could be employed in combination with self-supervised learning methods to improve models for downstream tasks.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Image Processing (ICIP)
PublisherInstitute of Electrical and Electronics Engineers
Pages66-70
Number of pages5
ISBN (Electronic)978-1-6654-4115-5
DOIs
Publication statusPublished - 23 Aug 2021
Event28th IEEE International Conference on Image Processing (ICIP 2021) - Anchorage, United States
Duration: 19 Sep 202122 Sep 2021

Conference

Conference28th IEEE International Conference on Image Processing (ICIP 2021)
Abbreviated titleICIP 2021
Country/TerritoryUnited States
CityAnchorage
Period19/09/2122/09/21

Keywords

  • Representation Learning
  • Self-Supervised Learning
  • Convolutional Neural Networks
  • Computer Aided Diagnosis
  • Endoscopy

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