collaborators

7 papers

eess.IV2019

Learning Fixed Points in Generative Adversarial Networks: From Image-to-Image Translation to Disease Detection and Localization

Md Mahfuzur Rahman Siddiquee, Zongwei Zhou, Nima Tajbakhsh +4

Generative adversarial networks (GANs) have ushered in a revolution in image-to-image translation. The development and proliferation of GANs raises an interesting question: can we…

eess.IV2019

Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis

Zongwei Zhou, Vatsal Sodha, Md Mahfuzur Rahman Siddiquee +4

Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this parad…

eess.IV2018

Robust Real-time Ellipse Fitting Based on Lagrange Programming Neural Network and Locally Competitive Algorithm

Hao Wang, Chi-Sing Leung, Hing Cheung So +3

Given a set of 2-dimensional (2-D) scattering points, which are usually obtained from the edge detection process, the aim of ellipse fitting is to construct an elliptic equation th…

eess.SP2018

Robust MIMO Radar Target Localization based on Lagrange Programming Neural Network

Hao Wang, Chi-Sing Leung, Hing Cheung So +3

This paper focuses on target localization in a widely distributed multiple-input-multiple-output (MIMO) radar system. In this system, range measurements, which include the sum of d…

eess.SP2018

Fast L1-Minimization Algorithm for Sparse Approximation Based on an Improved LPNN-LCA framework

Hao Wang, Ruibin Feng, Chi-Sing Leung

The aim of sparse approximation is to estimate a sparse signal according to the measurement matrix and an observation vector. It is widely used in data analytics, image processing,…

cs.LG2018

l0-norm Based Centers Selection for Training Fault Tolerant RBF Networks and Selecting Centers

Hao Wang, Chi-Sing Leung, Hing Cheung So +2

The aim of this paper is to train an RBF neural network and select centers under concurrent faults. It is well known that fault tolerance is a very attractive property for neural n…