Science Score: 67.0%
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Low similarity (11.4%) to scientific vocabulary
Repository
Synthesis Models for Multi-Sequence MRIs
Basic Info
- Host: GitHub
- Owner: fiy2W
- License: mit
- Language: Python
- Default Branch: main
- Size: 4.05 MB
Statistics
- Stars: 36
- Watchers: 1
- Forks: 4
- Open Issues: 0
- Releases: 0
Metadata Files
README.md
Seq2Seq: Sequence-to-Sequence Generator
We are committed to exploring the application of synthesis for multi-sequence MRI (also including other modalities such as CT) in clinical settings.
Seq2Seq is a series of dynamic multi-domain models that can translate an arbitrary sequence to a target sequence. - To learn more information about our work, please refer to our publications. - If you are looking for a straightforward way to resolve image-to-image tasks (e.g., synthesis and segmentation) without much thought, please try our nnSeq2Seq.
Publications
If you use Seq2Seq or some part of the code, please cite (see bibtex):
Seq2Seq: an arbitrary sequence to a target sequence synthesis, the sequence contribution ranking, and associated imaging-differentiation maps.
Synthesis-based Imaging-Differentiation Representation Learning for Multi-Sequence 3D/4D MRI
Medical Image Analysis.TSF-Seq2Seq: an explainable task-specific synthesis network, which adapts weights automatically for specific sequence generation tasks and provides interpretability and reliability.
An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis
MICCAI2023.VQ-Seq2Seq: a generative model that compresses discrete representations of each sequence to estimate the Gaussian distribution of vector-quantized common (VQC) latent space between multiple sequences.
Non-Adversarial Learning: Vector-Quantized Common Latent Space for Multi-Sequence MRI MICCAI2024.
nnSeq2Seq (beta)
Referring to nnU-Net, we propose nnSeq2Seq, a tool for adaptively training Seq2Seq models with a given dataset. It will analyze the provided training cases and automatically configure a matching synthesis pipeline. No expertise is required on your end! You can easily train the models and use them for your application.
How to get started?
Read these: - Installation instructions - Dataset conversion - Usage instructions
Examples
Solution of challenges: - MAMA-MIA
Acknowledgements
Contact
For any code-related problems or questions please open an issue or concat us by emails.
- Ritse.Mann@radboudumc.nl (Ritse Mann)
- taotan@mpu.edu.mo (Tao Tan)
- Luyi.Han@radboudumc.nl (Luyi Han)
Owner
- Name: Luyi
- Login: fiy2W
- Kind: user
- Website: https://fiy2w.github.io/
- Repositories: 2
- Profile: https://github.com/fiy2W
蜀道之难，难于上青天！
Citation (citations.bib)
% Seq2Seq
@article{han2024synthesis,
title={Synthesis-based imaging-differentiation representation learning for multi-sequence 3D/4D MRI},
author={Han, Luyi and Tan, Tao and Zhang, Tianyu and Huang, Yunzhi and Wang, Xin and Gao, Yuan and Teuwen, Jonas and Mann, Ritse},
journal={Medical Image Analysis},
volume={92},
pages={103044},
year={2024},
publisher={Elsevier}
}
% TSF-Seq2Seq
@inproceedings{han2023explainable,
title={An Explainable Deep Framework: Towards Task-Specific Fusion for Multi-to-One MRI Synthesis},
author={Han, Luyi and Zhang, Tianyu and Huang, Yunzhi and Dou, Haoran and Wang, Xin and Gao, Yuan and Lu, Chunyao and Tan, Tao and Mann, Ritse},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={45--55},
year={2023},
organization={Springer}
}
% VQ-Seq2Seq
@inproceedings{han2024non,
title={Non-adversarial Learning: Vector-Quantized Common Latent Space for Multi-sequence MRI},
author={Han, Luyi and Tan, Tao and Zhang, Tianyu and Wang, Xin and Gao, Yuan and Lu, Chunyao and Liang, Xinglong and Dou, Haoran and Huang, Yunzhi and Mann, Ritse},
booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
pages={481--491},
year={2024},
organization={Springer}
}
GitHub Events
Total
- Issues event: 2
- Watch event: 4
- Issue comment event: 4
- Push event: 4
Last Year
- Issues event: 2
- Watch event: 4
- Issue comment event: 4
- Push event: 4
Dependencies
- SimpleITK >=2.2.1
- acvl-utils >=0.2,<0.3
- batchgenerators >=0.25
- dicom2nifti *
- dynamic-network-architectures >=0.2,<0.4
- einops *
- graphviz *
- imagecodecs *
- lpips *
- matplotlib *
- nibabel *
- numpy *
- pandas *
- requests *
- scikit-image >=0.19.3
- scikit-learn *
- scipy *
- seaborn *
- tifffile *
- timm *
- torch >=2.0.0
- torchvision *
- tqdm *
- yacs *