Machine and deep learning methods for radiomics-Reference-Cited by-同舟云学术

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Machine and deep learning methods for radiomics

Published:2020-05 Issue:5 Volume:47 Page:
ISSN:0094-2405
Container-title:Medical Physics
language:en
Short-container-title:Med. Phys.

Author:

Avanzo Michele¹,Wei Lise²,Stancanello Joseph³,Vallières Martin⁴⁵,Rao Arvind²⁶,Morin Olivier⁵,Mattonen Sarah A.⁷,El Naqa Issam²

Affiliation:

1. Department of Medical Physics Centro di Riferimento Oncologico di Aviano (CRO) IRCCS Aviano PN 33081 Italy

2. Department of Radiation Oncology University of Michigan Ann Arbor MI 48103USA

3. Guerbet SA Villepinte France

4. Medical Physics Unit McGill University Montreal QC Canada

5. Department of Radiation Oncology University of California, San Francisco San Francisco CA 94143USA

6. Department of Computational Medicine & Bioinformatics University of Michigan Ann Arbor MI 48103USA

7. Department of Radiology Stanford University Stanford CA 94305USA

Publisher

Wiley

Subject

General Medicine

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.13678

Reference147 articles.

1. Beyond imaging: The promise of radiomics

2. Radiomics: Extracting more information from medical images using advanced feature analysis

3. Decoding tumour phenotype by noninvasive imaging using a quantitative radiomics approach

4. Is there a causal relationship between genetic changes and radiomics-based image features? An in vivo preclinical experiment with doxycycline inducible GADD34 tumor cells

5. Radiomics: the process and the challenges

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