AI in Medicine Books
Published September 12, 2025 | Version v1.0.0
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3D ViT in Medical Imaging From Theory to TensorFlow Practice

Authors/Creators

  • 1. Independent Researcher

Description

The present book, “3D ViT in Medical Imaging: From Theory to TensorFlow Practice,” builds upon the foundations established in my previous work, “Vision Transformers in Medical Imaging: Foundations and Applications.”

While the earlier book introduced the principles of Vision Transformers and their integration into medical imaging, this volume takes a decisive step forward by focusing on three-dimensional medical data. CT, MRI, and PET scans embody the true complexity of human anatomy, and addressing them requires models that go beyond two-dimensional slices.

Here, I aim to provide not only the theoretical underpinnings of 3D Vision Transformers but also practical implementations with TensorFlow and Keras, ensuring that researchers, students, and clinicians can bring these models directly into real-world applications.

My aspiration is that this book will inspire continued innovation, helping the medical AI community advance toward faster diagnoses, more precise treatments, and a future where technology and healthcare work hand in hand to improve global well-being.

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3D ViT in Medical Imaging From Theory to TensorFlow Practice.pdf

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Related works

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Book: 10.5281/zenodo.16901885 (DOI)

References

  • "Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., … Houlsby, N. (2020). An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. arXiv preprint arXiv:2010.11929.", "Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., … Polosukhin, I. (2017). Attention is All You Need. Advances in Neural Information Processing Systems (NeurIPS), 30.", "Chen, J., Lu, Y., Yu, Q., Luo, X., Adeli, E., Wang, Y., … Zhou, Y. (2021). TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation. arXiv preprint arXiv:2102.04306.", "Hatamizadeh, A., Tang, Y., Nath, V., Yang, D., Roth, H. R., Xu, D., … Xu, Z. (2022). UNETR: Transformers for 3D Medical Image Segmentation. Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (WACV), 574–584.", "He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.", "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(2), 203–211.", "Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., … Guo, B. (2021). Swin Transformer: Hierarchical Vision Transformer using Shifted Windows. Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), 10012–10022.", "Selvaraju, R. R., Cogswell, M., Das, A., Vedantam, R., Parikh, D., & Batra, D. (2017). Grad-CAM: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conference on Computer Vision (ICCV), 618–626.", "Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., … Sánchez, C. I. (2017). A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42, 60–88.", "Zhou, Z., Siddiquee, M. M. R., Tajbakhsh, N., & Liang, J. (2018). UNet++: A Nested U-Net Architecture for Medical Image Segmentation. arXiv preprint arXiv:1807.10165.", "Çiçek, Ö., Abdulkadir, A., Lienkamp, S. S., Brox, T., & Ronneberger, O. (2016). 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 424–432.", "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), 565–571.", "Ronneberger, O., Fischer, P., & Brox, T. (2015). U-Net: Convolutional Networks for Biomedical Image Segmentation. Medical Image Computing and Computer-Assisted Intervention (MICCAI), 234–241.", "Gao, Y., Zhou, M., Metaxas, D., & Zhang, S. (2022). Convolutional Vision Transformers. Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 12271–12281.", "Kermany, D. S., Goldbaum, M., Cai, W., Valentim, C. C. S., Liang, H., Baxter, S. L., … Zhang, K. (2018). Identifying Medical Diagnoses and Treatable Diseases by Image-Based Deep Learning. Cell, 172(5), 1122–1131.e9."