  In this work, we introduce Progressive Growing of Patch Size, a
resource-efficient implicit curriculum learning approach for dense prediction
tasks. Our curriculum approach is defined by growing the patch size during
model training, which gradually increases the task's difficulty. We integrated
our curriculum into the nnU-Net framework and evaluated the methodology on all
10 tasks of the Medical Segmentation Decathlon. With our approach, we are able
to substantially reduce runtime, computational costs, and CO2 emissions of
network training compared to classical constant patch size training. In our
experiments, the curriculum approach resulted in improved convergence. We are
able to outperform standard nnU-Net training, which is trained with constant
patch size, in terms of Dice Score on 7 out of 10 MSD tasks while only spending
roughly 50% of the original training runtime. To the best of our knowledge, our
Progressive Growing of Patch Size is the first successful employment of a
sample-length curriculum in the form of patch size in the field of computer
vision. Our code is publicly available at
https://github.com/compai-lab/2024-miccai-fischer.
