Literature DB >> 29254765

On the Fuzziness of Machine Learning, Neural Networks, and Artificial Intelligence in Radiation Oncology.

Issam El Naqa, Kristy Brock, Yan Yu, Katja Langen, Eric E Klein.   

Abstract

Entities:  

Mesh：

Year:  2018        PMID： 29254765     DOI： 10.1016/j.ijrobp.2017.06.011

Source DB:  PubMed          Journal:  Int J Radiat Oncol Biol Phys        ISSN： 0360-3016            Impact factor:   7.038


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  5 in total

1.  Paired cycle-GAN-based image correction for quantitative cone-beam computed tomography.

Authors:  Joseph Harms; Yang Lei; Tonghe Wang; Rongxiao Zhang; Jun Zhou; Xiangyang Tang; Walter J Curran; Tian Liu; Xiaofeng Yang
Journal:  Med Phys       Date:  2019-07-17       Impact factor: 4.071

Review 2.  Genomics models in radiotherapy: From mechanistic to machine learning.

Authors:  John Kang; James T Coates; Robert L Strawderman; Barry S Rosenstein; Sarah L Kerns
Journal:  Med Phys       Date:  2020-06       Impact factor: 4.071

3.  Synthetic CT Generation Based on T2 Weighted MRI of Nasopharyngeal Carcinoma (NPC) Using a Deep Convolutional Neural Network (DCNN).

Authors:  Yuenan Wang; Chenbin Liu; Xiao Zhang; Weiwei Deng
Journal:  Front Oncol       Date:  2019-11-29       Impact factor: 6.244

Review 4.  Radiomics for radiation oncologists: are we ready to go?

Authors:  Loïg Vaugier; Ludovic Ferrer; Laurence Mengue; Emmanuel Jouglar
Journal:  BJR Open       Date:  2020-03-25

5.  Radiomics Analysis of 3D Dose Distributions to Predict Toxicity of Radiotherapy for Cervical Cancer.

Authors:  François Lucia; Vincent Bourbonne; Dimitris Visvikis; Omar Miranda; Dorothy M Gujral; Dominique Gouders; Gurvan Dissaux; Olivier Pradier; Florent Tixier; Vincent Jaouen; Julien Bert; Mathieu Hatt; Ulrike Schick
Journal:  J Pers Med       Date:  2021-05-11
  5 in total

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