Cited by 235Radiomics and Machine Learning in Medical ImagingArtificial Intelligence in Healthcare and EducationAI in cancer detection

The DOI matches its Crossref record; 159 of the 162 references it cites were found in a registry or an index.

Artificial intelligence: Deep learning in oncological radiomics and challenges of interpretability and data harmonization

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Abstract

Over the last decade there has been an extensive evolution in the Artificial Intelligence (AI) field. Modern radiation oncology is based on the exploitation of advanced computational methods aiming to personalization and high diagnostic and therapeutic precision. The quantity of the available imaging data and the increased developments of Machine Learning (ML), particularly Deep Learning (DL), triggered the research on uncovering "hidden" biomarkers and quantitative features from anatomical and functional medical images. Deep Neural Networks (DNN) have achieved outstanding performance and broad implementation in image processing tasks. Lately, DNNs have been considered for radiomics and their potentials for explainable AI (XAI) may help classification and prediction in clinical practice. However, most of them are using limited datasets and lack generalized applicability. In this study we review the basics of radiomics feature extraction, DNNs in image analysis, and major interpretability methods that help enable explainable AI. Furthermore, we discuss the crucial requirement of multicenter recruitment of large datasets, increasing the biomarkers variability, so as to establish the potential clinical value of radiomics and the development of robust explainable AI models.

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Research works printed but not linked: 6

  1. Radiomics, 2016, there is more than meets the eye in medical imaging, Proceedings of the SPIE., 9785
  2. Hatt, 2019, Data are also images, J Nucl Med., 60, 38S, 10.2967/jnumed.118.220582 · DOI
  3. Distill. Feature Visualization: How neural networks build up their understanding of images. https://distill.pub/2017/feature-visualization/. Accessed on November 28, 2020.
  4. Samek, 2017, Evaluating the Visualization of What a Deep Neural Network Has Learned, IEEE Trans Neural Netw Learn Syst., 28, 2660, 10.1109/TNNLS.2016.2599820 · DOI · Paper page
  5. Pinto Dos Santos, 2020, A decade of radiomics research: are images really data or just patterns in the noise?, Eur Radiol.
  6. Chatterjee, 2019, Creating robust predictive radiomic models for data from independent institutions using normalization. IEEE Trans Radiat Plasma, Med Sci., 1

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Metadata

Authors
Papadimitroulas, P., Brocki, L., Christopher Chung, N., Marchadour, W., Vermet, F., Gaubert, L., Eleftheriadis, V., Plachouris, D., Visvikis, D., Kagadis, G. C., & Hatt, M.
Affiliations
  • University of Patras (GR)
  • University of Warsaw (PL)
  • Inserm (FR)
  • Centre National de la Recherche Scientifique (FR)
Corresponding author
Kagadis, George C.
Venue
Physica Medica
Publisher
Elsevier BV
Published
2021-03
Volume
83
Pages
108-121
ISSN
1120-1797 (ISSN-L)
OpenAlex
W3138961261
Cited by
226
References
162
Citations by year
2026: 29 · 2025: 64 · 2024: 42 · 2023: 40 · 2022: 40 · 2021: 11
FWCI
5.51
Type
journal article
Language
en
Open access
Locations
  • Physica Medica (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion)
Funders
  • European Commission (MIS- 5033021, 872735)
  • Agence Nationale de la Recherche (ANR-11-LABX-0020)
  • State Scholarships Foundation (MIS‐5033021, 5033021)
  • European Social Fund (MIS - 5033021, 5033021)
  • H2020 Marie Skłodowska-Curie Actions (872735)
  • CHIST-ERA (CHIST-ERA-19-XAI-007)
Topics
  • Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Keywords
radiomics, deep learning, explainable AI, interpretability, data harmonization, radiation oncology, deep neural network, radiomic feature extraction, multi-center dataset, biomarkers, image analysis
MeSH terms
Machine Learning, Deep Learning, Artificial Intelligence, Image Processing, Computer-Assisted, Multicenter Studies as Topic, Neural Networks, Computer

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