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Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling
This item is licensed under:Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International
| Title: | Boosting automatic COVID-19 detection performance with self-supervised learning and batch knowledge ensembling |
| Authors: | Li, Guang Browse this author | | Togo, Ren Browse this author →KAKEN DB | | Ogawa, Takahiro Browse this author →KAKEN DB | | Haseyama, Miki Browse this author →KAKEN DB |
| Keywords: | COVID-19 | | CXR images | | Self-supervised learning | | Batch knowledge ensembling |
| Issue Date: | 3-Apr-2023 |
| Publisher: | Elsevier |
| Journal Title: | Computers in biology and medicine |
| Volume: | 158 |
| Start Page: | 106877 |
| Publisher DOI: | 10.1016/j.compbiomed.2023.106877 |
| Abstract: | Problem: Detecting COVID-19 from chest X-ray (CXR) images has become one of the fastest and easiest methods for detecting COVID-19. However, the existing methods usually use supervised transfer learning from natural images as a pretraining process. These methods do not consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia. Aim: In this paper, we want to design a novel high-accuracy COVID-19 detection method that uses CXR images, which can consider the unique features of COVID-19 and the similar features between COVID-19 and other pneumonia.Methods: Our method consists of two phases. One is self-supervised learning-based pertaining; the other is batch knowledge ensembling-based fine-tuning. Self-supervised learning-based pretraining can learn dis-tinguished representations from CXR images without manually annotated labels. On the other hand, batch knowledge ensembling-based fine-tuning can utilize category knowledge of images in a batch according to their visual feature similarities to improve detection performance. Unlike our previous implementation, we introduce batch knowledge ensembling into the fine-tuning phase, reducing the memory used in self-supervised learning and improving COVID-19 detection accuracy.Results: On two public COVID-19 CXR datasets, namely, a large dataset and an unbalanced dataset, our method exhibited promising COVID-19 detection performance. Our method maintains high detection accuracy even when annotated CXR training images are reduced significantly (e.g., using only 10% of the original dataset). In addition, our method is insensitive to changes in hyperparameters. Conclusion: The proposed method outperforms other state-of-the-art COVID-19 detection methods in different settings. Our method can reduce the workloads of healthcare providers and radiologists. |
| Rights: | © 2023. This manuscript version is made available under the CC-BY-NC-ND 4.0 license https://creativecommons.org/licenses/by-nc-nd/4.0/ | | https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| Type: | article (author version) |
| URI: | http://hdl.handle.net/2115/91735 |
| Appears in Collections: | 数理・データサイエンス教育研究センター (Education and Research Center for Mathematical and Data Science) > 雑誌発表論文等 (Peer-reviewed Journal Articles, etc)
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Submitter: Li Guang(李 広)
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