Please use this identifier to cite or link to this item:
http://hdl.handle.net/1893/36756
Appears in Collections: | Computing Science and Mathematics Journal Articles |
Peer Review Status: | Refereed |
Title: | Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
Author(s): | Ali, Hazrat Qureshi, Rizwan Shah, Zubair |
Contact Email: | ali.hazrat@stir.ac.uk |
Keywords: | artificial intelligence AI brain cancer brain tumour medical imaging segmentation vision transformers |
Issue Date: | 17-Nov-2023 |
Date Deposited: | 27-Jan-2025 |
Citation: | Ali H, Qureshi R & Shah Z (2023) Artificial Intelligence-Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review. <i>JMIR Medical Informatics</i>, 11, Art. No.: e47445. https://doi.org/10.2196/47445 |
Abstract: | Background: Transformer-based models are gaining popularity in medical imaging and cancer imaging applications. Many recent studies have demonstrated the use of transformer-based models for brain cancer imaging applications such as diagnosis and tumor segmentation. Objective: This study aims to review how different vision transformers (ViTs) contributed to advancing brain cancer diagnosis and tumor segmentation using brain image data. This study examines the different architectures developed for enhancing the task of brain tumor segmentation. Furthermore, it explores how the ViT-based models augmented the performance of convolutional neural networks for brain cancer imaging. Methods: This review performed the study search and study selection following the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. The search comprised 4 popular scientific databases: PubMed, Scopus, IEEE Xplore, and Google Scholar. The search terms were formulated to cover the interventions (ie, ViTs) and the target application (ie, brain cancer imaging). The title and abstract for study selection were performed by 2 reviewers independently and validated by a third reviewer. Data extraction was performed by 2 reviewers and validated by a third reviewer. Finally, the data were synthesized using a narrative approach. Results: Of the 736 retrieved studies, 22 (3%) were included in this review. These studies were published in 2021 and 2022. The most commonly addressed task in these studies was tumor segmentation using ViTs. No study reported early detection of brain cancer. Among the different ViT architectures, Shifted Window transformer–based architectures have recently become the most popular choice of the research community. Among the included architectures, UNet transformer and TransUNet had the highest number of parameters and thus needed a cluster of as many as 8 graphics processing units for model training. The brain tumor segmentation challenge data set was the most popular data set used in the included studies. ViT was used in different combinations with convolutional neural networks to capture both the global and local context of the input brain imaging data. Conclusions: It can be argued that the computational complexity of transformer architectures is a bottleneck in advancing the field and enabling clinical transformations. This review provides the current state of knowledge on the topic, and the findings of this review will be helpful for researchers in the field of medical artificial intelligence and its applications in brain cancer. |
DOI Link: | 10.2196/47445 |
Rights: | ©Hazrat Ali, Rizwan Qureshi, Zubair Shah. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 17.11.2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
Licence URL(s): | http://creativecommons.org/licenses/by/4.0/ |
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