2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

Imaging Without Borders

2021 IEEE International Conference on Image Processing

19-22 September 2021 • Anchorage, Alaska, USA

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Paper Detail

Paper IDBIO-2.2
Paper Title EVALUATING SELF-SUPERVISED LEARNING METHODS FOR DOWNSTREAM CLASSIFICATION OF NEOPLASIA IN BARRETT'S ESOPHAGUS
Authors Stefan Cornelissen, Joost van der Putten, Tim Boers, Eindhoven University of Technology, Netherlands; Jelmer Jukema, Kiki Fockens, Jacques Bergman, Amsterdam University Medical Centers, Netherlands; Fons van der Sommen, Peter de With, Eindhoven University of Technology, Netherlands
SessionBIO-2: Biomedical Signal Processing 2
LocationArea D
Session Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Time:Tuesday, 21 September, 08:00 - 09:30
Presentation Poster
Topic Biomedical Signal Processing: Medical image analysis
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
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.