Transformers for Neuroimage Segmentation: Scoping Review

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

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