4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection
Abbas, Asmaa and Abdelsamea, Mohammed M. and Gaber, Mohamed Medhat (2021) 4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection. IEEE Transactions on Neural Networks and Learning Systems, 32 (7). pp. 2798-2808. ISSN 2162-237X
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Abstract
Due to the high availability of large-scale annotated image datasets, knowledge transfer from pretrained models showed outstanding performance in medical image classification. However, building a robust image classification model for datasets with data irregularity or imbalanced classes can be a very challenging task, especially in the medical imaging domain. In this article, we propose a novel deep convolutional neural network,which we called self-supervised super sample decomposition for transfer learning (4S-DT) model. The 4S-DT encourages a coarse-to-fine transfer learning from large-scale image recognition tasks to a specific chest X-ray image classification task using a generic self-supervised sample decomposition approach. Our main contribution is a novel self-supervised learning mechanism guided by a super sample decomposition of unlabeled chest X-ray images. 4S-DT helps in improving the robustness of knowledge
transformation via a downstream learning strategy with a class decomposition (CD) layer to simplify the local structure of the data. The 4S-DT can deal with any irregularities in the image dataset by investigating its class boundaries using a downstream CD mechanism. We used 50 000 unlabeled chest X-ray images to achieve our coarse-to-fine transfer learning with an application to COVID-19 detection, as an exemplar. The 4S-DT has achieved a high accuracy of 99.8% on the larger of the two datasets used in the experimental study and an accuracy of 97.54% on the smaller dataset, which was enriched by augmented images, out of which all real COVID-19 cases were detected.
| Item Type: | Article | ||||||
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| Identification Number: | https://doi.org/10.1109/TNNLS.2021.3082015 | ||||||
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| Uncontrolled Keywords: | Chest X-ray image classification, convolutional neural network (CNN), data irregularities, self-supervision, transfer learning | ||||||
| Subjects: | CAH11 - computing > CAH11-01 - computing > CAH11-01-05 - artificial intelligence | ||||||
| Divisions: | Faculty of Computing, Engineering and the Built Environment > School of Computing and Digital Technology | ||||||
| Depositing User: | Mohammed Abdelsamea | ||||||
| Date Deposited: | 27 Oct 2021 12:24 | ||||||
| Last Modified: | 06 Jun 2022 14:14 | ||||||
| URI: | https://www.open-access.bcu.ac.uk/id/eprint/12322 |
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