SymmAdapt checkpoints (abdomen MRI → CT)
Weights for SymmAdapt: source-free domain adaptation of a SymmFlow (SymmFMSD) segmenter.
Code: github.com/tal-grossman/SymmAdapt_public
These files are the paper MRI→CT run (CHAOS MRI → BTCV CT, four organs: spleen, kidneys, liver).
| File | What it is |
|---|---|
source_mri_best.pt | Source model trained on labeled MRI (Phase I) |
gst_mri_to_ct_best.pt | After generative self-training on unlabeled CT (Phase III) |
Load them with the public repo (bash scripts/download_checkpoints.sh, then scripts/run_eval.sh). Do not expect a Hugging Face from_pretrained API — they are PyTorch trainer checkpoints (ema_state_dict).
Not included here:
- LatFM init: ocaetas/SymmFlow (
LatFM_coco_beta6.0.pt) - SD VAE:
stabilityai/sd-vae-ft-mse(pulled at runtime) - Images: DFG / BTCV / CHAOS
License
MIT
Citation
@misc{grossman2026symmadaptsymmetricalflowmatching,
title={SymmAdapt: Symmetrical Flow Matching for Source-Free Domain Adaptation in Medical Image Segmentation},
author={Tal Grossman and Noa Cahan and Hayit Greenspan},
year={2026},
eprint={2608.22532},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2608.22532},
}
Also cite SymmFlow if you use these weights:
@article{caetano2025symmetrical,
title = {Symmetrical Flow Matching: Unified Image Generation, Segmentation, and Classification with Score-Based Generative Models},
author = {Caetano, Francisco and Viviers, Christiaan and De With, Peter HN and van der Sommen, Fons},
journal = {arXiv preprint arXiv:2506.10634},
year = {2025}
}