Artificial Intelligence in radiotherapy: state of the art and future directions-Reference-Cited by-同舟云学术

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Artificial Intelligence in radiotherapy: state of the art and future directions

Published:2020-04-22 Issue:6 Volume:37 Page:
ISSN:1357-0560
Container-title:Medical Oncology
language:en
Short-container-title:Med Oncol

Author:

Francolini Giulio,Desideri Isacco,Stocchi Giulia,Salvestrini Viola,Ciccone Lucia Pia,Garlatti Pietro,Loi Mauro,Livi Lorenzo

Publisher

Springer Science and Business Media LLC

Subject

Cancer Research,Oncology,Hematology,General Medicine

Link

https://link.springer.com/content/pdf/10.1007/s12032-020-01374-w.pdf

Reference51 articles.

1. El Naqa I, et al. Artificial Intelligence: reshaping the practice of radiological sciences in the 21st century. Br J Radiol. 2020;93:1106.

2. Meyer P, et al. Survey on deep learning for radiotherapy. Comput Biol Med. 2018;98:126–46.

3. Sharp G, et al. Vision 20/20: perspectives on automated image segmentation for radiotherapy. Med Phys. 2014;5:050902.

4. Eldesoky AR, et al. Internal and external validation of an ESTRO delineation guideline: dependent automated segmentation tool for loco-regional radiation therapy of early breast cancer. Radiother Oncol. 2016;3:424–30.

5. Nguyen A et al. Deep neural networks are easily fooled: high confidence predictions for unrecognizable images. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit; 2015; pp. 427–436.

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