This work has been early accepted by MICCAI 2026. The paper and BibTeX entry will be updated once the arXiv preprint or official proceedings version is available.
Datasets: BraTS (BraTS25-GLIPRE as source; BraTS23-SSA and BraTS24-PED as target). Set csv_path and save_dir in configs/dataset/*.yaml and configs/task/*.yaml to your data and output paths.
Setup: pip install -r requirements.txt
Train source model (e.g. BraTS):
python main.py task=brats_source dataset=brats model=unet training=default
Run TTA (example):
python main.py task=brats_ours dataset=brats_SSA model=unet tta.ckpt_path=/path/to/best_model.pth training.epochs=1 training.batch_size=1
See train_brats.sh.
If you find this repository useful, please consider citing our paper. The BibTeX entry will be added once the arXiv preprint or official MICCAI proceedings version is available.
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