Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis
This work addresses the environmental costs of deep learning in medical image analysis, which is an incremental contribution focusing on a specific domain.
The study tackled the problem of high energy consumption and carbon footprint in deep learning for medical image analysis by quantifying the carbon footprint of segmentation pipelines using nnU-net on three datasets, and discussed strategies to reduce environmental impact.
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.