Standard
Operating Critical Machine Learning Models in Resource Constrained Regimes. / Selvan, Raghavendra; Schön, Julian; Dam, Erik B.
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings. Springer, 2024. s. 325-335 Chapter 29 (Lecture Notes in Computer Science, Bind 14394).
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Selvan, R, Schön, J & Dam, EB 2024,
Operating Critical Machine Learning Models in Resource Constrained Regimes. i
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings., Chapter 29, Springer, Lecture Notes in Computer Science, bind 14394, s. 325-335, 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023, Vancouver, Canada,
08/10/2023.
https://doi.org/10.1007/978-3-031-47425-5_29
APA
Selvan, R., Schön, J., & Dam, E. B. (2024).
Operating Critical Machine Learning Models in Resource Constrained Regimes. I
Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings (s. 325-335). [Chapter 29] Springer. Lecture Notes in Computer Science Bind 14394
https://doi.org/10.1007/978-3-031-47425-5_29
Vancouver
Selvan R, Schön J, Dam EB.
Operating Critical Machine Learning Models in Resource Constrained Regimes. I Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings. Springer. 2024. s. 325-335. Chapter 29. (Lecture Notes in Computer Science, Bind 14394).
https://doi.org/10.1007/978-3-031-47425-5_29
Author
Selvan, Raghavendra ; Schön, Julian ; Dam, Erik B. / Operating Critical Machine Learning Models in Resource Constrained Regimes. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops: MTSAIL 2023, LEAF 2023, AI4Treat 2023, MMMI 2023, REMIA 2023, Held in Conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8–12, 2023, Proceedings. Springer, 2024. s. 325-335 (Lecture Notes in Computer Science, Bind 14394).
Bibtex
@inproceedings{e8440101184b46ceb4c07acb155b5d5f,
title = "Operating Critical Machine Learning Models in Resource Constrained Regimes",
abstract = "The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.",
author = "Raghavendra Selvan and Julian Sch{\"o}n and Dam, {Erik B.}",
year = "2024",
doi = "10.1007/978-3-031-47425-5_29",
language = "English",
isbn = "978-3-031-47424-8",
series = "Lecture Notes in Computer Science",
publisher = "Springer",
pages = "325--335",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops",
address = "Switzerland",
note = "26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 ; Conference date: 08-10-2023 Through 12-10-2023",
}
RIS
TY - GEN
T1 - Operating Critical Machine Learning Models in Resource Constrained Regimes
AU - Selvan, Raghavendra
AU - Schön, Julian
AU - Dam, Erik B.
PY - 2024
Y1 - 2024
N2 - The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.
AB - The accelerated development of machine learning methods, primarily deep learning, are causal to the recent breakthroughs in medical image analysis and computer aided intervention. The resource consumption of deep learning models in terms of amount of training data, compute and energy costs are known to be massive. These large resource costs can be barriers in deploying these models in clinics, globally. To address this, there are cogent efforts within the machine learning community to introduce notions of resource efficiency. For instance, using quantisation to alleviate memory consumption. While most of these methods are shown to reduce the resource utilisation, they could come at a cost in performance. In this work, we probe into the trade-off between resource consumption and performance, specifically, when dealing with models that are used in critical settings such as in clinics.
U2 - 10.1007/978-3-031-47425-5_29
DO - 10.1007/978-3-031-47425-5_29
M3 - Article in proceedings
SN - 978-3-031-47424-8
T3 - Lecture Notes in Computer Science
SP - 325
EP - 335
BT - Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 Workshops
PB - Springer
T2 - 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023
Y2 - 8 October 2023 through 12 October 2023
ER -
