Standard
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis. / Selvan, Raghavendra; Bhagwat, Nikhil; Anthony, Lasse F. Wolff; Kanding, Benjamin; Dam, Erik B.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V. Springer, 2022. s. 506–516 (Lecture Notes in Computer Science, Bind 13435).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Selvan, R, Bhagwat, N, Anthony, LFW, Kanding, B
& Dam, EB 2022,
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis. i
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V. Springer, Lecture Notes in Computer Science, bind 13435, s. 506–516, 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022, Singapore, Singapore,
18/09/2022.
https://doi.org/10.1007/978-3-031-16443-9
APA
Selvan, R., Bhagwat, N., Anthony, L. F. W., Kanding, B.
, & Dam, E. B. (2022).
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis. I
Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V (s. 506–516). Springer. Lecture Notes in Computer Science Bind 13435
https://doi.org/10.1007/978-3-031-16443-9
Vancouver
Selvan R, Bhagwat N, Anthony LFW, Kanding B
, Dam EB.
Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis. I Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V. Springer. 2022. s. 506–516. (Lecture Notes in Computer Science, Bind 13435).
https://doi.org/10.1007/978-3-031-16443-9
Author
Selvan, Raghavendra ; Bhagwat, Nikhil ; Anthony, Lasse F. Wolff ; Kanding, Benjamin ; Dam, Erik B. / Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis. Medical Image Computing and Computer Assisted Intervention – MICCAI 2022: 25th International Conference Singapore, September 18–22, 2022 Proceedings, Part V. Springer, 2022. s. 506–516 (Lecture Notes in Computer Science, Bind 13435).
Bibtex
@inproceedings{68c7cd36998648d48c11206c8b056196,
title = "Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis",
abstract = " 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. ",
keywords = "eess.IV, cs.CV, cs.LG",
author = "Raghavendra Selvan and Nikhil Bhagwat and Anthony, {Lasse F. Wolff} and Benjamin Kanding and Dam, {Erik B.}",
year = "2022",
doi = "10.1007/978-3-031-16443-9",
language = "English",
isbn = " 978-3-031-16442-2",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "506–516",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2022",
address = "Switzerland",
note = "25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022 ; Conference date: 18-09-2022 Through 22-09-2022",
}
RIS
TY - GEN
T1 - Carbon Footprint of Selecting and Training Deep Learning Models for Medical Image Analysis
AU - Selvan, Raghavendra
AU - Bhagwat, Nikhil
AU - Anthony, Lasse F. Wolff
AU - Kanding, Benjamin
AU - Dam, Erik B.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - eess.IV
KW - cs.CV
KW - cs.LG
U2 - 10.1007/978-3-031-16443-9
DO - 10.1007/978-3-031-16443-9
M3 - Article in proceedings
SN - 978-3-031-16442-2
T3 - Lecture Notes in Computer Science
SP - 506
EP - 516
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2022
PB - Springer
T2 - 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2022
Y2 - 18 September 2022 through 22 September 2022
ER -
