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Review
. 2024 Jan 10;16(2):300.
doi: 10.3390/cancers16020300.

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

Carla Pitarch  1   2 , Gulnur Ungan  3   4 , Margarida Julià-Sapé  3   4 , Alfredo Vellido  1   4
Affiliations
Review

Advances in the Use of Deep Learning for the Analysis of Magnetic Resonance Image in Neuro-Oncology

Carla Pitarch et al. Cancers (Basel). .

Abstract

Machine Learning is entering a phase of maturity, but its medical applications still lag behind in terms of practical use. The field of oncological radiology (and neuro-oncology in particular) is at the forefront of these developments, now boosted by the success of Deep-Learning methods for the analysis of medical images. This paper reviews in detail some of the most recent advances in the use of Deep Learning in this field, from the broader topic of the development of Machine-Learning-based analytical pipelines to specific instantiations of the use of Deep Learning in neuro-oncology; the latter including its use in the groundbreaking field of ultra-low field magnetic resonance imaging.

Keywords: data analysis pipeline; deep learning; machine learning; neuro-oncology; radiology; ultra-low field magnetic resonance imaging.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

Figures

Figure 1
Figure 1
Dataset usage prevalence across the reviewed literature.
Figure 2
Figure 2
Yearly inclusion of articles in this review that focus on classifying brain tumors using DL and MRI scans.
See this image and copyright information in PMC

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