Evaluation of Explainable AI Methods in CNN Classifiers of COVID-19 CT Images
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Publisher
Springer Nature Switzerland
Link
https://link.springer.com/content/pdf/10.1007/978-3-031-49404-8_31
Reference18 articles.
1. Lee, E.H., Zheng, J., Colak, E., et al.: Deep COVID DeteCT: an international experience on COVID-19 lung detection and prognosis using chest CT. NPJ Digit. Med. 4, 11 (2021). https://doi.org/10.1038/s41746-020-00369-1
2. Roberts, M., Driggs, D., Thorpe, M., et al.: Common pitfalls and recommendations for using machine learning to detect and prognosticate for COVID-19 using chest radiographs and CT scans. Nat. Mach. Intell. 3, 199–217 (2021). https://doi.org/10.1038/s42256-021-00307-0
3. Singh, A., Sengupta, S., Lakshminarayanan, V.: Explainable deep learning models in medical image analysis. J. Imaging 6 (2020). https://doi.org/10.48550/arXiv.2005.13799
4. Gunraj, H., Wang, L., Wong, A.: COVIDNet-CT: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest CT images. Front. Med. 7 (2020). https://doi.org/10.3389/fmed.2020.608525
5. Joshua, E., Bhattacharyya, D., Chakkravarthy, M., Byun, Y.: 3D CNN with visual insights for early detection of lung cancer using gradient-weighted class activation. J. Healthcare Eng. 1–11 (2021). https://doi.org/10.1155/2021/6695518
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