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.

翻译：本文提出了一种渐进式增大图像块尺寸的方法，这是一种面向密集预测任务的资源高效隐式课程学习策略。我们的课程学习方法通过在模型训练过程中逐步增大输入图像块的尺寸，从而渐进式提升任务难度。我们将该课程学习策略集成到nnU-Net框架中，并在医学分割十项全能挑战赛的全部10个任务上进行了系统性评估。与传统固定尺寸图像块训练方法相比，本方法能显著减少训练时间、计算资源消耗及二氧化碳排放量。实验结果表明，该课程学习策略有效改善了模型收敛性能。在十项全能挑战赛的10个任务中，本方法在7个任务上的Dice分数超越了采用固定图像块尺寸的标准nnU-Net训练方法，同时仅需约50%的原始训练时间。据我们所知，渐进式增大图像块尺寸是计算机视觉领域首次成功采用以图像块尺寸为载体的样本长度课程学习方法。相关代码已开源：https://github.com/compai-lab/2024-miccai-fischer。