Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model-Reference-Cited by-同舟云学术

Feature Density as an Uncertainty Estimator Method in the Binary Classification Mammography Images Task for a Supervised Deep Learning Model

Published:2022 Issue: Volume: Page:375-388
ISSN:0302-9743
Container-title:Bioinformatics and Biomedical Engineering
language:
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Author:

Fuentes-Fino Ricardo Javier,Calderón-Ramírez Saúl,Domínguez Enrique,López-Rubio Ezequiel,Hernandez-Vasquez Marco A.,Molina-Cabello Miguel A.

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-07802-6_32

Reference19 articles.

1. Calderon-Ramirez, S., et al.: Improving uncertainty estimation with semi-supervised deep learning for Covid-19 detection using chest x-ray images. IEEE Access 9, 85, 442–485 (2021)

2. Calderon-Ramirez, S., et al.: Dealing with scarce labelled data: semi-supervised deep learning with mix match for Covid-19 detection using chest x-ray images. In: 2020 25th International Conference on Pattern Recognition (ICPR), pp. 5294–5301. IEEE (2021)

3. Wild, C., Weiderpass, E., Stewart, B.: World cancer report: cancer research for cancer prevention. International Agency for Research on Cancer, Lyon, France (2020)

4. A. C. Society: Breast Cancer Facts & Figures 2019–2020. American Cancer Society, Atlanta (2019)

5. Lecture Notes in Computer Science;MA Molina-Cabello,2019

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