Operating Critical Machine Learning Models in Resource Constrained Regimes
Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review
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Accepted author manuscript, 868 KB, PDF document
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.
Original language | English |
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Title of host publication | 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 |
Publisher | Springer |
Publication date | 2024 |
Pages | 325-335 |
Article number | Chapter 29 |
ISBN (Print) | 978-3-031-47424-8 |
ISBN (Electronic) | 978-3-031-47425-5 |
DOIs | |
Publication status | Published - 2024 |
Event | 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 - Vancouver, Canada Duration: 8 Oct 2023 → 12 Oct 2023 |
Conference
Conference | 26th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2023 |
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Land | Canada |
By | Vancouver |
Periode | 08/10/2023 → 12/10/2023 |
Series | Lecture Notes in Computer Science |
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Volume | 14394 |
ISSN | 0302-9743 |
ID: 383097264