Deep reinforcement learning for automated radiation adaptation in lung cancer-Reference-Cited by-同舟云学术

Deep reinforcement learning for automated radiation adaptation in lung cancer

Published:2017-11-14 Issue:12 Volume:44 Page:6690-6705
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Tseng Huan-Hsin¹,Luo Yi¹,Cui Sunan¹,Chien Jen-Tzung¹²,Ten Haken Randall K.¹,Naqa Issam El¹

Affiliation:

1. Department of Radiation Oncology; University of Michigan; Ann Arbor MI USA

2. Department of Electrical and Computer Engineering; National Chiao Tung University; Hsinchu Taiwan

Funder

National Institutes of Health

Publisher

Wiley

Subject

General Medicine

Reference49 articles.

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4. Standard-dose versus high-dose conformal radiotherapy with concurrent and consolidation carboplatin plus paclitaxel with or without cetuximab for patients with stage iiia or iiib non-small-cell lung cancer (RTOG 0617): a randomised two-by-two factorial phase 3 study;Bradley;Lancet Oncol,2015

5. Naqa IEl Li R Murphy MJ Machine learning in radiation oncology Theory and Applications New York, NY Springer International Publishing

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