bibtext: @InProceedings{ Fis_Progressive_MICCAI2024,
                   author = { Fischer, Stefan M. and Felsner, Lina and Osuala, Richard and Kiechle, Johannes and Lang, Daniel M. and Peeken, Jan C. and Schnabel, Julia A. },
                   title = { { Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks } },
                   booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2024},
                   year = {2024},
                   publisher = {Springer Nature Switzerland},
                   volume = { LNCS 15009 },
                   month = {October},
                   pages = { pending },
                }
abstract: 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 CO$_{2}$ 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 \url{https://github.com}.
title: Progressive Growing of Patch Size: Resource-Efficient Curriculum Learning for Dense Prediction Tasks
authors: Fischer, Stefan M. Felsner, Lina Osuala, Richard Kiechle, Johannes Lang, Daniel M. Peeken, Jan C. Schnabel, Julia A.