Neuro-TransUNet: A comprehensive transformer-based architecture for precise segmentation of stroke lesions in 3D MRI

Author:

Nouman Muhammad1ORCID,Mabrok Mohamed2ORCID,Rashed Essam A.1ORCID

Affiliation:

1. Graduate School of Information Science, University of Hyogo, Japan

2. Department of Mathematics and Statistics, Qatar University, Qatar

Funder

Qatar-Japan Research Collaboration

Publisher

ACM

Reference29 articles.

1. A. Al-Kababji 2022. Scheduling techniques for liver segmentation: Reducelronplateau vs onecyclelr. In International Conference on Intelligent Systems and Pattern Recognition. Springer, 204–212.

2. Hu Cao, Yueyue Wang, Joy Chen, Dongsheng Jiang, Xiaopeng Zhang, Qi Tian, and Manning Wang. 2022. Swin-unet: Unet-like pure transformer for medical image segmentation. In European conference on computer vision. Springer, 205–218.

3. L. Chalcroft 2023. Large-kernel Attention for Efficient and Robust Brain Lesion Segmentation. arXiv preprint arXiv:2308.07251 (2023).

4. M. D. Cirillo 2021. What is the best data augmentation for 3D brain tumor segmentation?. In 2021 IEEE International Conference on Image Processing (ICIP). IEEE, 36–40.

5. P. Deb 2023. BeSt-LeS: Benchmarking Stroke Lesion Segmentation using Deep Supervision. arXiv preprint arXiv:2310.07060 (2023).

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