تقرير
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis
| العنوان: | Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis |
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| المؤلفون: | Selvan, Raghavendra, Bhagwat, Nikhil, Anthony, Lasse F. Wolff, Kanding, Benjamin, Dam, Erik B. |
| سنة النشر: | 2022 |
| المجموعة: | Computer Science |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning |
| الوصف: | 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. Comment: Accepted to be presented as an Oral Presentation at 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2022. 13 pages. 5 figures |
| نوع الوثيقة: | Working Paper |
| DOI: | 10.1007/978-3-031-16443-9_49 |
| الوصول الحر: | http://arxiv.org/abs/2203.02202Test |
| رقم الانضمام: | edsarx.2203.02202 |
| قاعدة البيانات: | arXiv |
| DOI: | 10.1007/978-3-031-16443-9_49 |
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