Vision Transformers in Medical Imaging: Foundations and Applications
Description
This book is dedicated to providing a comprehensive understanding of Vision Transformers (ViTs) and their applications in medical imaging. In recent years, transformer-based models have reshaped the landscape of computer vision, offering unprecedented accuracy and adaptability. Their integration into medical imaging has opened new horizons for diagnosis, prognosis, and treatment planning.
Despite this remarkable progress, there remains a lack of structured and accessible resources tailored to medical applications of ViTs. The purpose of this work is to bridge that gap by delivering clear explanations, illustrative examples, and practical guidance for students, researchers, and professionals.
It is my hope that this book will serve not only as a technical reference but also as a source of inspiration, encouraging further innovation at the intersection of artificial intelligence and medical imaging.
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- Journal article: 10.1101/2025.08.05.25333032 (DOI)
- Journal article: 10.1101/2025.08.12.25333539 (DOI)
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References
- Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., Uszkoreit, J., & Houlsby, N. (2021). An Image is Worth 16×16 Words: Transformers for Image Recognition at Scale. International Conference on Learning Representations (ICLR). arXiv:2010.11929; Touvron, H., Cord, M., Jégou, H., & Douze, M. (2021). Training data-efficient image transformers & distillation through attention. International Conference on Machine Learning (ICML). arXiv:2012.12877; Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Mottaghi, R. (2020). End-to-End Object Detection with Transformers. European Conference on Computer Vision (ECCV). arXiv:2005.12872; Zhu, X., & Han, S. (2021). Deformable DETR: Deformable Transformers for End-to-End Object Detection. International Conference on Learning Representations (ICLR). arXiv:2010.04159; Zhang, H., & Wang, X. (2021). CrossViT: Cross-Attention Multi-Scale Vision Transformer for Image Classification. IEEE/CVF International Conference on Computer Vision (ICCV). arXiv:2103.14899; Chen, J., & Xie, L. (2021). ViTDet: Vision Transformer for Object Detection. arXiv:2103.11886; Li, Y., & Li, Z. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. IEEE/CVF International Conference on Computer Vision (ICCV). arXiv:2103.14030; Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., & Guo, B. (2021). Swin Transformer V2: Scaling Up Capacity and Resolution. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). arXiv:2111.09883; Dosovitskiy, A., & Brox, T. (2015). Inverting Visual Representations with Convolutional Networks. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI). arXiv:1411.5922; Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. A., Kaiser, Ł., & Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems (NeurIPS). arXiv:1706.03762; Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI). Springer; Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press; Chollet, F. (2017). Deep Learning with Python. Manning Publications; Isensee, F., Jaeger, P. F., Kohl, S. A. A., Petersen, J., & Maier-Hein, K. H. (2021). nnU-Net: A Self-Adapting Framework for U-Net-Based Medical Image Segmentation. Nature Methods, 18, 203–211; Milletari, F., Navab, N., & Ahmadi, S. A. (2016). V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation. 2016 Fourth International Conference on 3D Vision (3DV). IEEE Xplore