Document Type
Article
Publication Date
7-29-2023
Keywords
vision transformer (ViT); self-supervised learning; chest X-ray image; image classification
Abstract
The COVID-19 pandemic has underscored the urgent need for rapid and accurate diagnosis facilitated by artificial intelligence (AI), particularly in computer-aided diagnosis using medical imag- ing. However, this context presents two notable challenges: high diagnostic accuracy demand and limited availability of medical data for training AI models. To address these issues, we proposed the implementation of a Masked AutoEncoder (MAE), an innovative self-supervised learning approach, for classifying 2D Chest X-ray images. Our approach involved performing imaging reconstruction using a Vision Transformer (ViT) model as the feature encoder, paired with a custom-defined decoder. Additionally, we fine-tuned the pretrained ViT encoder using a labeled medical dataset, serving as the backbone. To evaluate our approach, we conducted a comparative analysis of three distinct training methods: training from scratch, transfer learning, and MAE-based training, all employing COVID-19 chest X-ray images. The results demonstrate that MAE-based training produces superior performance, achieving an accuracy of 0.985 and an AUC of 0.9957. We explored the mask ratio influ- ence on MAE and found ratio = 0.4 shows the best performance. Furthermore, we illustrate that MAE exhibits remarkable efficiency when applied to labeled data, delivering comparable performance to utilizing only 30% of the original training dataset. Overall, our findings highlight the significant performance enhancement achieved by using MAE, particularly when working with limited datasets. This approach holds profound implications for future disease diagnosis, especially in scenarios where imaging information is scarce.
ORCID ID
https://orcid.org/0000-0002-6700-6664
Digital Object Identifier (DOI)
https://doi.org/10.3390/bioengineering10080901
Repository Citation
Xing, Xin; Liang, Gongbo; Wang, Chris; Jacobs, Nathan; and Lin, Ai-Ling, "Self-Supervised Learning Application on COVID-19 Chest X- ray Image Classification Using Masked AutoEncoder" (2023). All Faculty Scholarship. 20.
https://digitalcommons.tamusa.edu/pubs_faculty/20
Comments
Originally published as:
Xing, X.; Liang, G.; Wang, C.; Jacobs, N.; Lin, A.-L. Self-Supervised Learning Application on COVID-19 Chest X-ray Image Classification Using Masked AutoEncoder. Bioengineering 2023, 10, 901. https://doi.org/10.3390/bioengineering10080901
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).