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.