The increasing energy consumption and carbon footprint of deep learning (DL) due to growing compute requirements has become a cause of concern. In this work, we focus on the carbon footprint of developing DL models for medical image analysis (MIA), where volumetric images of high spatial resolution are handled. In this study, we present and compare the features of four tools from literature to quantify the carbon footprint of DL. Using one of these tools we estimate the carbon footprint of medical image segmentation pipelines. We choose nnU-net as the proxy for a medical image segmentation pipeline and experiment on three common datasets. With our work we hope to inform on the increasing energy costs incurred by MIA. We discuss simple strategies to cut-down the environmental impact that can make model selection and training processes more efficient.
翻译:在这项工作中,我们把重点放在开发用于医学图像分析的DL模型(MIA)的碳足迹上,该模型处理高空间分辨率的体积图像。在本研究中,我们介绍并比较了四种文献工具的特征,以量化DL的碳足迹。我们利用其中一种工具来估计医疗图像分割管道的碳足迹。我们选择nnU-net作为医学图像分割管道和三个共同数据集实验的代用品。我们希望通过我们的工作来了解MAA产生的能源成本不断增加的情况。我们讨论了减少环境影响的简单战略,这种战略可以提高选择和培训过程的示范效率。