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Cited by 302Advanced Radiotherapy TechniquesArtificial Intelligence in Healthcare and EducationRadiomics and Machine Learning in Medical Imaging

The DOI matches its Crossref record; 230 of the 237 references it cites were found in a registry or an index.

Survey on deep learning for radiotherapy

Meyer, Philippe, Noblet, Vincent, Mazzara, Christophe, Lallement, Alex·2018·Computers in Biology and Medicine
DOI 10.1016/j.compbiomed.2018.05.018
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Abstract

More than 50% of cancer patients are treated with radiotherapy, either exclusively or in combination with other methods. The planning and delivery of radiotherapy treatment is a complex process, but can now be greatly facilitated by artificial intelligence technology. Deep learning is the fastest-growing field in artificial intelligence and has been successfully used in recent years in many domains, including medicine. In this article, we first explain the concept of deep learning, addressing it in the broader context of machine learning. The most common network architectures are presented, with a more specific focus on convolutional neural networks. We then present a review of the published works on deep learning methods that can be applied to radiotherapy, which are classified into seven categories related to the patient workflow, and can provide some insights of potential future applications. We have attempted to make this paper accessible to both radiotherapy and deep learning communities, and hope that it will inspire new collaborations between these two communities to develop dedicated radiotherapy applications.

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Crossref · 10.1016/j.compbiomed.2018.05.018

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Citations

Cites

Show all 237 references

Research works printed but not linked: 6

  1. Bengio, 2009, Learning deep architectures for AI, found. Trends® mach, Learning, 2, 1
  2. de Brebisson, 2015, Deep neural networks for anatomical brain segmentation, in: proc. IEEE Conf. Comput. Vis. Pattern Recognit, Works, 20
  3. Whitey, 2008, A review of medial image segmentation, Int. J. Bioelectromagn, 10, 125
  4. MICCAI BRATS - The Multimodal Brain Tumor Segmentation Challenge. https://www.med.upenn.edu/sbia/brats2018.html.
  5. Yuan, 2017, Automatic skin lesion segmentation using deep fully convolutional networks with jaccard distance, IEEE Trans. Med. Imaging. PP, 10.1109/TMI.2017.2695227 · DOI · Paper page
  6. Belharbi, 2017, Spotting L3 slice in CT scans using deep convolutional network and transfer learning, Comput. Biol. Med., 10.1016/j.compbiomed.2017.05.018 · DOI

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Metadata

Authors
Meyer, P., Noblet, V., Mazzara, C., & Lallement, A.
Affiliations
  • Institut Strauss (FR)
  • Laboratoire des Sciences de l'Ingénieur, de l'Informatique et de l'Imagerie (FR)
Corresponding author
Meyer, Philippe
ORCID
Venue
Computers in Biology and Medicine
Publisher
Elsevier BV
Published
2018-07
Volume
98
Pages
126-146
ISSN
0010-4825 (ISSN-L)
DOI
10.1016/j.compbiomed.2018.05.018
OpenAlex
W2804608955
Cited by
302
References
237
Citations by year
2026: 11 · 2025: 13 · 2024: 38 · 2023: 38 · 2022: 46 · 2021: 60 · 2020: 48 · 2019: 37 · 2018: 11
FWCI
5.52
Type
journal article
Language
en
Open access
closed
Licence
www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations
  • Computers in Biology and Medicine (publishedVersion)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
Topics
  • Physical Sciences › Physics and Astronomy › Radiation › Advanced Radiotherapy Techniques
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Keywords
deep learning, radiotherapy, radiotherapy treatment planning, convolutional neural networks, artificial intelligence in healthcare, machine learning, network architecture, patient workflow
MeSH terms
Deep Learning, Humans, Nasopharyngeal Neoplasms, Radiotherapy, Radiotherapy Planning, Computer-Assisted

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