Cross-Domain Transfer Learning for Medical Condition Classification from Infant X-ray Images
DOI:
https://doi.org/10.47611/jsrhs.v13i1.6231Keywords:
infant X-ray, transfer learning, representation learningAbstract
Field of artificial intelligence technology has flourished in recent years which led to a creation of diagnosing or assessing diseases using X-ray images. On the downside, these creations focus mostly on adult X-ray images and can not accurately diagnose infant X-ray images. This issue stems from a combination of factors: limited availability of datasets containing infant X-ray and significant variation in these images due to the rapid development of the infant body. Therefore, there is a high demand to develop comprehensive solutions that address these challenges and provide accurate insights. The proposed representation learning-based framework comprises two stages: auto-encoder-based representation learning and transfer learning for diagnosis. The first stage uses adult X-ray images to train the model for improved representation, generating identical reconstructed images. The second stage utilizes pre-trained models to diagnose diseases and predict infant age, enhancing accuracy by accounting for age-related variations in X-ray shapes. This innovative approach represents the first endeavor in unrestricted pediatric X-ray diagnosis, utilizing self-supervised learning for enhanced accuracy. As a result, the comprehensive and extensive experiment allows the proposed method to outperform in comparison to the existing methods. I expect that my research will contribute to the pediatric field of medicine and serve as the foundation of diverging the utility of artificial intelligence.
Downloads
References or Bibliography
AI Hub (2023, May 1). “Pediatric abdominal x-ray image data”: AI Hub
Bhardwaj, Shivam. (2021, Jun 8). “Convolutional Neural Networks : Understand the Basics”: Analytics Vidhya
https://www.analyticsvidhya.com/blog/2021/05/convolutional-neural-networks-understand-the-basics/
Fran, C. (2017). Deep learning with depth wise separable convolutions. In IEEE conference on computer vision and pattern recognition (CVPR). https://doi.org/10.48550/arXiv.1610.02357
He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). https://doi.org/10.48550/arXiv.1512.03385
Huang, G., Liu, Z., Van Der Maaten, L., & Weinberger, K. Q. (2017). Densely connected convolutional networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4700-4708). https://doi.org/10.48550/arXiv.1608.06993
Pham, H. H., Nguyen, N. H., Tran, T. T., Nguyen, T. N., & Nguyen, H. Q. (2023). PediCXR: An open, large-scale chest radiograph dataset for interpretation of common thoracic diseases in children. Scientific Data, 10(1), 240.
Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., & Chen, L. C. (2018). Mobilenetv2: Inverted residuals and linear bottlenecks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 4510-4520). https://doi.org/10.48550/arXiv.1801.04381
Simonyan, K., & Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. https://doi.org/10.48550/arXiv.1409.1556
Wang, J., Sun, K., Cheng, T., Jiang, B., Deng, C., Zhao, Y., ... & Xiao, B. (2020). Deep high-resolution representation learning for visual recognition. IEEE transactions on pattern analysis and machine intelligence, 43(10), 3349-3364. https://doi.org/10.48550/arXiv.1908.07919
Xie, S., Girshick, R., Dollár, P., Tu, Z., & He, K. (2017). Aggregated residual transformations for deep neural networks. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 1492-1500).
https://doi.org/10.48550/arXiv.1611.05431
Zhang, X., Zhou, X., Lin, M., & Sun, J. (2018). Shufflenet: An extremely efficient convolutional neural network for mobile devices. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 6848-6856).
Published
How to Cite
Issue
Section
Copyright (c) 2024 Seyoung Park; Joe Martin
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Copyright holder(s) granted JSR a perpetual, non-exclusive license to distriute & display this article.
