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 language | English |
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Title of host publication | 2021 IEEE International Conference on Image Processing, ICIP 2021 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers |
Pages | 66-70 |
Number of pages | 5 |
ISBN (Electronic) | 978-1-6654-4115-5 |
DOIs | |
Publication status | Published - 23 Aug 2021 |
Event | 28th IEEE International Conference on Image Processing (ICIP 2021) - Anchorage, United States Duration: 19 Sep 2021 → 22 Sep 2021 |
Conference
Conference | 28th IEEE International Conference on Image Processing (ICIP 2021) |
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Abbreviated title | ICIP 2021 |
Country/Territory | United States |
City | Anchorage |
Period | 19/09/21 → 22/09/21 |
Keywords
- Representation Learning
- Self-Supervised Learning
- Convolutional Neural Networks
- Computer Aided Diagnosis
- Endoscopy
- Representation learning
- Self-supervised learning
- Computer aided diagnosis
- Convolutional neural networks