Introducing first version of semi-supervised functionality #3
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This code introduces semi-supervised functionality to cellulus. It is designed to run in a similar way to train.py.
A semi_supervised_experiment_config and semi_supervised_train_config can be created, pointing to the datasets for pseudo-labels (output of cellulus), ground-truth-labels (other annotations), and raw images.
semi_supervised_train.py will then train a UNet to predict a stardist representation from your cell images. The pseudo-gt and gt labels are combined, such that GT have precedence over pseudo-GT (though this can be changed manually).
Does not yet include a predict script! this is still being worked on.