activity
20182022
most citedA Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models

2 citations · 2 across the 3 of their papers we have counts for

collaborators

6 papers

cs.SD2022

Individualized Conditioning and Negative Distances for Speaker Separation

Tao Sun, Nidal Abuhajar, Shuyu Gong +5

Speaker separation aims to extract multiple voices from a mixed signal. In this paper, we propose two speaker-aware designs to improve the existing speaker separation solutions. Th…

eess.IV20222 cited

A Comparative Study on 1.5T-3T MRI Conversion through Deep Neural Network Models

Binhua Liao, Yani Chen, Zhewei Wang +2

In this paper, we explore the capabilities of a number of deep neural network models in generating whole-brain 3T-like MR images from clinical 1.5T MRIs. The models include a fully…

cs.CV2019

Residual Pyramid FCN for Robust Follicle Segmentation

Zhewei Wang, Weizhen Cai, Charles D. Smith +3

In this paper, we propose a pyramid network structure to improve the FCN-based segmentation solutions and apply it to label thyroid follicles in histology images. Our design is bas…

cs.CV2019

TraceCaps: A Capsule-based Neural Network for Semantic Segmentation

Tao Sun, Zhewei Wang, C. D. Smith +1

In this paper, we propose a capsule-based neural network model to solve the semantic segmentation problem. By taking advantage of the extractable part-whole dependencies available…

cs.CV2018

Ensemble of Multi-sized FCNs to Improve White Matter Lesion Segmentation

Zhewei Wang, Charles D. Smith, Jundong Liu

In this paper, we develop a two-stage neural network solution for the challenging task of white-matter lesion segmentation. To cope with the vast vari- ability in lesion sizes, we…

cs.LG2018

Nonlinear Metric Learning through Geodesic Interpolation within Lie Groups

Zhewei Wang, Bibo Shi, Charles D. Smith +1

In this paper, we propose a nonlinear distance metric learning scheme based on the fusion of component linear metrics. Instead of merging displacements at each data point, our mode…