3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation
Paschali M, Gasperini S, Roy AG, Fang MYS, Navab N (2019)
Publication Type: Conference contribution
Publication year: 2019
Journal
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 11766 LNCS
Pages Range: 438-446
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Shenzhen, CHN
ISBN: 9783030322472
DOI: 10.1007/978-3-030-32248-9_49
Abstract
Model architectures have been dramatically increasing in size, improving performance at the cost of resource requirements. In this paper we propose 3DQ, a ternary quantization method, applied for the first time to 3D Fully Convolutional Neural Networks, enabling 16x model compression while maintaining performance on par with full precision models. We extensively evaluate 3DQ on two datasets for the challenging task of whole brain segmentation. Additionally, we showcase the ability of our method to generalize on two common 3D architectures, namely 3D U-Net and V-Net. Outperforming a variety of baselines, the proposed method is capable of compressing large 3D models to a few MBytes, alleviating the storage needs in space-critical applications.
Involved external institutions
Technische Universität München (TUM)
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
University of California, Berkeley
United States (USA) (US)
Germany (DE)
Ludwig-Maximilians-Universität (LMU)
Germany (DE)
University of California, Berkeley
United States (USA) (US)
How to cite
APA:
Paschali, M., Gasperini, S., Roy, A.G., Fang, M.Y.-S., & Navab, N. (2019). 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation. In Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 438-446). Shenzhen, CHN: Springer Science and Business Media Deutschland GmbH.
MLA:
Paschali, Magdalini, et al. "3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation." Proceedings of the 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Shenzhen, CHN Ed. Dinggang Shen, Pew-Thian Yap, Tianming Liu, Terry M. Peters, Ali Khan, Lawrence H. Staib, Caroline Essert, Sean Zhou, Springer Science and Business Media Deutschland GmbH, 2019. 438-446.
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