Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review
<h3>Background</h3><p dir="ltr">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...
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| المؤلف الرئيسي: | |
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| مؤلفون آخرون: | , |
| منشور في: |
2023
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| _version_ | 1864513520763142144 |
|---|---|
| author | Hazrat Ali (421019) |
| author2 | Rizwan Qureshi (15279193) Zubair Shah (231886) |
| author2_role | author author |
| author_facet | Hazrat Ali (421019) Rizwan Qureshi (15279193) Zubair Shah (231886) |
| author_role | author |
| dc.creator.none.fl_str_mv | Hazrat Ali (421019) Rizwan Qureshi (15279193) Zubair Shah (231886) |
| dc.date.none.fl_str_mv | 2023-03-20T15:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/47445 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/Artificial_Intelligence_Based_Methods_for_Integrating_Local_and_Global_Features_for_Brain_Cancer_Imaging_Scoping_Review/26772214 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Health sciences Health services and systems artificial intelligence AI brain cancer brain tumor medical imaging segmentation vision transformers |
| dc.title.none.fl_str_mv | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">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.</p><h3>Objective</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Medical Informatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/47445" target="_blank">https://dx.doi.org/10.2196/47445</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_56f2d6151fc222e4678b2f6638840622 |
| identifier_str_mv | 10.2196/47445 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/26772214 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping ReviewHazrat Ali (421019)Rizwan Qureshi (15279193)Zubair Shah (231886)Biomedical and clinical sciencesClinical sciencesOncology and carcinogenesisHealth sciencesHealth services and systemsartificial intelligenceAIbrain cancerbrain tumormedical imagingsegmentationvision transformers<h3>Background</h3><p dir="ltr">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.</p><h3>Objective</h3><p dir="ltr">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.</p><h3>Methods</h3><p dir="ltr">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.</p><h3>Results</h3><p dir="ltr">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.</p><h3>Conclusions</h3><p dir="ltr">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.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Medical Informatics<br>License: <a href="https://creativecommons.org/licenses/by/4.0" target="_blank">https://creativecommons.org/licenses/by/4.0</a><br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/47445" target="_blank">https://dx.doi.org/10.2196/47445</a></p>2023-03-20T15:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/47445https://figshare.com/articles/journal_contribution/Artificial_Intelligence_Based_Methods_for_Integrating_Local_and_Global_Features_for_Brain_Cancer_Imaging_Scoping_Review/26772214CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/267722142023-03-20T15:00:00Z |
| spellingShingle | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review Hazrat Ali (421019) Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Health sciences Health services and systems artificial intelligence AI brain cancer brain tumor medical imaging segmentation vision transformers |
| status_str | publishedVersion |
| title | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| title_full | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| title_fullStr | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| title_full_unstemmed | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| title_short | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| title_sort | Artificial Intelligence–Based Methods for Integrating Local and Global Features for Brain Cancer Imaging: Scoping Review |
| topic | Biomedical and clinical sciences Clinical sciences Oncology and carcinogenesis Health sciences Health services and systems artificial intelligence AI brain cancer brain tumor medical imaging segmentation vision transformers |