Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis
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.
Code (0)
등록된 구현이 없습니다.
Tasks
Image SegmentationMedical Image AnalysisMedical Image SegmentationModel SelectionSegmentationSemantic SegmentationSimilar Papers 제목 키워드 기반
EcoLearn: Optimizing the Carbon Footprint of Federated Learning
Federated Learning (FL) distributes machine learning (ML) training across edge devices to reduce data transfer overhead and protect data privacy. Since FL model training may span hundreds of devices and is thus resource-…
Federated LearningA User Study of Perceived Carbon Footprint
We propose a statistical model to understand people's perception of their carbon footprint. Driven by the observation that few people think of CO2 impact in absolute terms, we design a system to probe people's perception…
Active LearningLatent Pollution Model: The Hidden Carbon Footprint in 3D Image Synthesis
Contemporary developments in generative AI are rapidly transforming the field of medical AI. These developments have been predominantly driven by the availability of large datasets and high computing power, which have fa…
Image GenerationLLMCarbon: Modeling the end-to-end Carbon Footprint of Large Language Models
The carbon footprint associated with large language models (LLMs) is a significant concern, encompassing emissions from their training, inference, experimentation, and storage processes, including operational and embodie…
GPUMixture-of-ExpertsChasing Low-Carbon Electricity for Practical and Sustainable DNN Training
Deep learning has experienced significant growth in recent years, resulting in increased energy consumption and carbon emission from the use of GPUs for training deep neural networks (DNNs). Answering the call for sustai…