Published March 18, 2026
| Version v1
Dataset
Open
YODA / Regression is all you need for medical image translation
Authors/Creators
-
1.
German Center for Neurodegenerative Diseases
-
2.
Massachusetts General Hospital
-
3.
Harvard Medical School
Description
Model weights and singularity/apptainer environment for YODA (Regression is all you need for medical image translation, 2026, IEEE T-MI) and extensions (ISMRM 2026).
Please note the usage instruction at github.com/Deep-MI/YODA. The provided dagobah.sif file is a pre-build Singularity/Apptainer container, ie the result from singularity build dagobah.sif docker://srassmann/dif:latest.
If you use the resources in your research, please always cite the YODA paper + checkpoint-specific additional (conference) papers.
| Checkpoint | Trans. Task | Resolution | Train. Dataset | Train. Paradigm | Citation(s) |
rs_FLAIR_from_T1T2.zip |
T1w+T2w -> FLAIR | 1 mm | RS (n=1344) | Diffusion | YODA |
brats_FLAIR_from_T1T2.zip |
T1w+T2w -> FLAIR | 1 mm (resampled) | BraTS '23 (n=1270) | Diffusion | YODA |
rs_FLAIR_from_T1.zip |
T1w -> FLAIR | 1 mm | RS (n=1344) | Diffusion | YODA, ISMRM 2026 (FLAIR) |
rs_FLAIR_from_T2.zip |
T2w -> FLAIR | 1 mm | RS (n=1344) | Diffusion | YODA, ISMRM 2026 (FLAIR) |
ixi_T2_from_T1PD.zip |
T1w+PD -> T2w | ~1 mm | IXI (n=511) | Diffusion | YODA |
GoldAtlas_CT_from_MR.zip |
T1w+T2w -> CT | ~1x1x3 mm | Gold Atlas (n=11) | Diffusion | YODA |
rs_T1_from_FLAIR.zip |
FLAIR -> T1w | 1 mm | RS (n=2500) | Regression | YODA, ISMRM 2026 (T1w) |
rs_T1_from_T2w.zip |
FLAIR -> T2w | 1 mm | RS (n=2500) | Regression | YODA, ISMRM 2026 (T1w) |
Citations:
YODA: Rassmann et al. (2026) "Regression is all you need for medical image translation", IEEE Transactions on Medical Imaging
ISMRM 2026 (FLAIR): Rassmann et al. (2026) "FLAIR-less white-matter hyperintensity segmentation using YODA", ISMRM 2026 (Cape Town)
ISMRM 2026 (T1w): Rassmann et al. (2026) "MRI contrast translation for full-brain segmentation from T2-weighted contrasts", ISMRM 2026 (Cape Town)
Note: Depending on the Training Paradigm, the checkpoints require either the diffusion (
dm_predict.py) or regression (reg_predict.py) inference scripts.The file
expected_output-FLAIR_from_T1T2.nii.gz is obtained from running the respective T1w+T2w->FLAIR translator on the example RS case.Files
rs_FLAIR_from_T1T2.zip
Files
(18.1 GB)
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Additional details
Related works
- Is supplement to
- Journal article: 10.1109/TMI.2025.3650412 (DOI)
Software
- Repository URL
- https://github.com/Deep-MI/YODA/