A pytorch implementation of the paper 'Multi-rater Prism: Learning self-calibrated medical image segmentation from multiple raters' and 'Learning self-calibrated optic disc and cup segmentation from multi-rater annotations' accepted by MICCAI 2022
The code is run on pytorch1.8.1 + cuda 10.1.
python train.py -net transunet -exp_name test_train -mod rec
python val.py -net transunet -mod rec -exp_name val_seg -weights 'recorded weights'
See cfg.py for more avaliable parameters
- add requirement
- del debug code
- function name alignment
- del redundance
- release a slim version