Accessibility settings

This paper is in the following e-collection/theme issue:

Artificial Intelligence (2374) Digital Health Reviews (1835) Neurology and Neurosciences (303) Imaging Informatics (335) Medical imaging (135) Advancements in Neuroimaging (18) AI in Neurotechnology (10)

Published on in Vol 27 (2025)

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/57723, first published .
Transformers for Neuroimage Segmentation: Scoping Review

Transformers for Neuroimage Segmentation: Scoping Review

Transformers for Neuroimage Segmentation: Scoping Review

Authors of this article:

Maya Iratni1 Author Orcid Image ;   Amira Abdullah1 Author Orcid Image ;   Mariam Aldhaheri1 Author Orcid Image ;   Omar Elharrouss1 Author Orcid Image ;   Alaa Abd-alrazaq2 Author Orcid Image ;   Zahiriddin Rustamov1 Author Orcid Image ;   Nazar Zaki1 Author Orcid Image ;   Rafat Damseh1 Author Orcid Image
Article Authors Cited by (11) Tweetations Metrics

Journals

  1. Satushe V, Vyas V, Metkar S, Singh D. AI in MRI brain tumor diagnosis: A systematic review of machine learning and deep learning advances (2010–2025). Chemometrics and Intelligent Laboratory Systems 2025;263:105414 View
  2. Alqadi M, Vidal S. Artificial Intelligence in Vascular Neurology: Applications, Challenges, and a Review of AI Tools for Stroke Imaging, Clinical Decision Making, and Outcome Prediction Models. Current Neurology and Neuroscience Reports 2025;25(1) View
  3. Zhang K, Hu Z, Wu S, Xiao L, Huang H. Transmednet: Transformer Medical Triad Neurology Networks. Concurrency and Computation: Practice and Experience 2025;37(23-24) View
  4. Liang P, Zeng Q, Liang B, Huang H, Zhang Y, Pu B, Chen J. WKPNet: A novel wavelet-KAN-POLA network for medical image segmentation. Biomedical Signal Processing and Control 2026;113:108988 View
  5. Ben Gara Ali M, Smiti A. Dynamic Swin-UNet: a transformer-based adaptive framework for precise and efficient Alzheimer’s disease brain segmentation. Multimedia Tools and Applications 2026;85(2) View
  6. Tariq U, Ahmed I, Shaukat K, Ihsan M. Systematic Review for Detecting & Diagnosing Brain Tumor Using Deep Learning: Current Trends and Future Research Directions. International Journal of Computational Intelligence Systems 2026 View
  7. Borno M, Shovon M, Sikder M, Rimi I, Alahmadi T, Moni M. Decentralized LoRA augmented transformer with multi-scale feature learning for secured eye diagnosis. Knowledge-Based Systems 2026:115948 View
  8. Ameri R, Shahmirzadi D, Yarahmadi A, Nadimi-Shahraki M. Vision transformers for neurological disorder diagnosis: a systematic review of architectural taxonomies, challenges, and future directions. Expert Systems with Applications 2026:132331 View

Books/Policy Documents

  1. Umamageswari A, Deepa S, Raja K. AI in Diagnostic Radiology. View

Conference Proceedings

  1. Dutta A, Ghosh S, Jana N. 2025 IEEE 9th International Conference on Information and Communication Technology (CICT). A Sequential Framework of Graph Neural Networks and Swin Transformers for Alzheimer’s Disease Stage Detection from 3D MRI Images View
  2. Sennouni S, Bara S, Laghbissi S, Minaoui K. 2025 IEEE International Conference on Advances in Data-Driven Analytics And Intelligent Systems (ADACIS). Comparative Analysis of Brain Tumor Segmentation Methods Using the BraTS Dataset (2020–2025) View