4 citations · 5 across the 4 of their papers we have counts for
4 papers
Post-Training Quantization for 3D Medical Image Segmentation: A Practical Study on Real Inference Engines
Chongyu Qu, Ritchie Zhao, Ye Yu +6
Quantizing deep neural networks ,reducing the precision (bit-width) of their computations, can remarkably decrease memory usage and accelerate processing, making these models more…
Polyhedra Encoding Transformers: Enhancing Diffusion MRI Analysis Beyond Voxel and Volumetric Embedding
Tianyuan Yao, Zhiyuan Li, Praitayini Kanakaraj +6
Diffusion-weighted Magnetic Resonance Imaging (dMRI) is an essential tool in neuroimaging. It is arguably the sole noninvasive technique for examining the microstructural propertie…
Multi-Modality Conditioned Variational U-Net for Field-of-View Extension in Brain Diffusion MRI
Zhiyuan Li, Chenyu Gao, Praitayini Kanakaraj +13
An incomplete field-of-view (FOV) in diffusion magnetic resonance imaging (dMRI) can severely hinder the volumetric and bundle analyses of whole-brain white matter connectivity. Al…
Field-of-View Extension for Brain Diffusion MRI via Deep Generative Models
Chenyu Gao, Shunxing Bao, Michael Kim +13
Purpose: In diffusion MRI (dMRI), the volumetric and bundle analyses of whole-brain tissue microstructure and connectivity can be severely impeded by an incomplete field-of-view (F…