Abstract
Computer-assisted treatment has emerged as a viable application of medical imaging, owing to the efficacy of deep learning models. Real-time inference speed remains a key requirement for such applications to help medical personnel. Even though there generally exists a trade-off between performance and model size, impressive efforts have been made to retain near-original performance by compromising model size. Neural network pruning has emerged as an exciting area that aims to eliminate redundant parameters to make the inference faster. In this study, we show an application of neural network pruning in polyp segmentation. We compute the importance score of convolutional filters and remove the filters having the least scores, which to some value of pruning does not degrade the performance. For computing the importance score we use the Taylor First Order (TaylorFO) approximation of the change in network output for the removal of certain filters. Specifically, we employ a gradient-normalized backpropagation for the computation of the importance score. Through experiments in the polyp datasets, we validate that our approach can significantly reduce the parameter count and FLOPs retaining similar performance.
Original language | English |
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Title of host publication | Medical Image Understanding and Analysis - 27th Annual Conference, MIUA 2023, Proceedings |
Editors | Gordon Waiter, Georgios Leontidis, Teresa Morris, Tryphon Lambrou, Nir Oren, Sharon Gordon |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 298-309 |
Number of pages | 12 |
ISBN (Print) | 9783031485923 |
DOIs | |
Publication status | Published - 2 Dec 2023 |
Event | 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023 - Aberdeen, United Kingdom Duration: 19 Jul 2023 → 21 Jul 2023 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 14122 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 27th Annual Conference on Medical Image Understanding and Analysis, MIUA 2023 |
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Country/Territory | United Kingdom |
City | Aberdeen |
Period | 19/07/23 → 21/07/23 |
Bibliographical note
Funding Information:This work is partly funded by the EndoMapper project by Horizon 2020 FET (GA 863146).
Keywords
- Neural Network Pruning
- Polyp Segmentation
- Real-time Colonoscopy