M. Paschali, S. Gasperini, A. Guha Roy, M.Y.-S. Fang, N. Navab 3DQ: Compact Quantized Neural Networks for Volumetric Whole Brain Segmentation The first two authors contributed equally. 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Shenzhen 2019. The original publication is available currently online on arXiv and will become available at link.springer.com (bib)
	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 (F-CNNs), 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 our method's ability 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.
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