4S-DT: Self-Supervised Super Sample Decomposition for Transfer Learning With Application to COVID-19 Detection

Abbas, Asmaa and Abdelsamea, Mohammed 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
Identification Number: https://doi.org/10.1109/TNNLS.2021.3082015
Dates:
DateEvent
17 May 2021Accepted
26 May 2021Published Online
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: 27 Oct 2021 12:25
URI: http://www.open-access.bcu.ac.uk/id/eprint/12322

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