COVID-19 Diagnosis in 3D Chest CT Scans with Attention-Based Models

1. Ardakani, A.A., Kanafi, A.R., Acharya, U.R., Khadem, N., Mohammadi, A.: Application of deep learning technique to manage covid- 19 in routine clinical practice using CT images: results of 10 convolutional neural networks. Computers in biology and medicine 121 (2020)

   Google Scholar 

2. Bertasius, G., Wang, H., Torresani, L.: Is space-time attention all you need for video understanding? In: ICML, vol. 2, p. 4 (2021)

   Google Scholar 

3. Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv:2010.11929 (2020)

4. He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770–778 (2016)

   Google Scholar 

5. He, X., et al.: Benchmarking deep learning models and automated model design for covid-19 detection with chest CT scans. MedRxiv (2021)

   Google Scholar 

6. Hatamizadeh, A., Tang, Y., Nath, V., et al.: Unetr: transformers for 3D medical image segmentation. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, pp. 574–584 (2022)

   Google Scholar 

7. Hsu, C.-C., Chen, G.-L., Wu, M.-H.: Visual transformer with statistical test for covid-19 classification. arXiv preprint arXiv:2107.05334 (2021)

8. Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700–4708 (2017)

   Google Scholar 

9. Kollias, D., Arsenos, A., Soukissian, L., Kollias, S.: Mia-cov19d: Covid-19 detection through 3-D chest CT image analysis. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 537–544 (2021)

   Google Scholar 

10. Li, L., Qin, L., Xu, Z., et al.: Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy. Radiology 296(2), 65–71 (2020)

    Article  Google Scholar 

11. Mishra, A.K., Das, S.K., Roy, P.: Bandyopadhyay, S.: Identifying covid19 from chest CT images: a deep convolutional neural networks based approach. J. Healthcare Eng. (2020)

    Google Scholar 

12. Pham, T.D.: A comprehensive study on classification of COVID-19 on computed tomography with pretrained convolutional neural networks. Sci. Rep. 10(1), 1–8 (2020)

    Article  Google Scholar 

13. Shakouri, S., et al.: Covid19-CT-dataset: an open-access chest CT image repository of 1000+ patients with confirmed covid-19 diagnosis. BMC Res Notes (2021)

    Google Scholar 

14. Shamshad, F., et al.: Transformers in medical imaging: a survey. arXiv preprint arXiv:2201.09873 (2022)

15. Shin, Y., Eo, T., Rha, H., et al.: Digestive Organ Recognition in Video Capsule Endoscopy Based on Temporal Segmentation Network. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds.) MICCAI 2022. LNCS, vol. 13437, pp. 136–146. Springer, Cham (2022). https://doi.org/10.1007/978-3-031-16449-1_14

16. Woolson, R.F.: Wilcoxon signed-rank test. Wiley encyclopedia of clinical trials, 1–3 (2007)

    Google Scholar 

17. Zhang, K., Liu, X., Shen, J., et al.: Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of covid-19 pneumonia using computed tomography. Cell 181(6), 1423–1433 (2020)

    Article  Google Scholar 

18. Zhang, L., Wen, Y.: A transformer-based framework for automatic covid19 diagnosis in chest CTs. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 513–518 (2021)

    Google Scholar 

Download references