Review for NeurIPS paper: Contrastive learning of global and local features for medical image segmentation with limited annotations

Weaknesses: There are a number of small weakness to the approach, the technique to some degree depends on well registered images and there are a number of extra hyper parameters introduced, such as the number of partitions to use per 3D volume, the number of pre-trained decoder blocks to use and the region size. I would expect these aspects to be largely problem dependent, and the degree to which results would also be improved on other problems is therefore somewhat unclear. I do not think this invalidates the above comment. Aside from the approach itself, I would also have liked some information on training time and convergence. How easy is this to setup, train and add to existing training processes?