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.
If you use Seq2Seq or some part of the code, please cite (see bibtex):
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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.
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.
Read these:
Solution of challenges:
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)