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Artificial intelligence in musculoskeletal oncological radiology Cover

Artificial intelligence in musculoskeletal oncological radiology

Radiology and Oncology
Volume 55 (2021): Issue 1 (March 2021)
By: Matjaz Vogrin,  Teodor Trojner and  Robi Kelc  
Open Access
|Nov 2020

Figures & Tables

Figure 1

Schematic illustration of the hierarchy of artificial intelligence and its machine learning and deep learning subfields.

Figure 2

Schematic presentation of a neural network. Regions of interests (ROI) are defined, either by user or by an automated computer process. These present the input cells (in pink) a neural network. For each ROI the neural network extracts and compute features within the hidden layers (in grey) by using pre-trained data sets. Finally, the output cell offers the final results in different possible forms (yes/no, final diagnosis, probability of malignancy etc.).
DOI: https://doi.org/10.2478/raon-2020-0068 | Journal eISSN: 1581-3207 | Journal ISSN: 1318-2099
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Language: English
Page range: 1 - 6
Submitted on: Jun 1, 2020
|
Accepted on: Sep 29, 2020
|
Published on: Nov 10, 2020
Published by: Association of Radiology and Oncology
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year
Keywords:
artificial intelligence,
deep learning,
tumor recognition,
cancer imaging,
image segmentation
Related subjects:
Medicine,
Clinical medicine,
Internal medicine,
Haematology, oncology,
Radiology

© 2020 Matjaz Vogrin, Teodor Trojner, Robi Kelc, published by Association of Radiology and Oncology
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

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