Uncertainty Propagation and Salient Features Maps in Deep Learning Architectures for Supporting Covid-19 Diagnosis-Reference-Cited by-同舟云学术

Uncertainty Propagation and Salient Features Maps in Deep Learning Architectures for Supporting Covid-19 Diagnosis

Published:2022 Issue: Volume: Page:1-22
ISSN:1860-949X
Container-title:Artificial Intelligence and Machine Learning Methods in COVID-19 and Related Health Diseases
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Author:

Rodríguez-García Iñaki^ORCID,Sánchez-Pastor Tomás^ORCID,Vázquez-Escobar Julia^ORCID,Gómez-González Juan Luis^ORCID,Cárdenas-Montes Miguel^ORCID

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-04597-4_1

Reference44 articles.

1. Abdar, M., Pourpanah, F., Hussain, S., Rezazadegan, D., Liu, L., Ghavamzadeh, M., & Nahavandi, S. (2021). A review of uncertainty quantification in deep learning: Techniques, applications and challenges. Information Fusion, 76, 243–297. Retrieved from https://doi.org/10.1016/j.inffus.2021.05.008, https://doi.org/10.1016/j.inffus.2021.05.008

2. Alizadehsani, R., Roshanzamir, M., Hussain, S., Khosravi, A., Koohestani, A., Zangooei, M. H., & Acharya, U. R. (2021). Handling of uncertainty in medical data using machine learning and probability theory techniques: A review of 30 years (1991–2020). Annals of Operations Research. Retrieved from https://doi.org/10.1007/s10479-021-04006-2, https://doi.org/10.1007/s10479-021-04006-2

3. Arora, S., Bhaskara, A., Ge, R., & Ma, T. (2014, 22-24 Jun). Provable bounds for learning some deep representations. In E. P. Xing & T. Jebara (Eds.), Proceedings References 19 of the 31st international conference on machine learning (Vol. 32, pp. 584-592). Bejing, China: PMLR.

4. Basu, S., Mitra, S., & Saha, N. (2020). Deep learning for screening covid-19 using chest x-ray images. In 2020 IEEE symposium series on computational intelligence (ssci) (pp. 2521–2527).

5. Bauer, S., Kohler, S., Schulz, M. H., & Robinson, P. N. (2012). Bayesian ontology querying for accurate and noise-tolerant semantic searches. Bioinformatics, 28(19), 2502–2508. Retrieved from https://doi.org/10.1093/bioinformatics/bts471, https://doi.org/10.1093/bioinformatics/bts471