57 citations · 110 across the 4 of their papers we have counts for
6 papers
Adversarial Deep Structured Nets for Mass Segmentation from Mammograms
Wentao Zhu, Xiang Xiang, Trac D. Tran +2
Mass segmentation provides effective morphological features which are important for mass diagnosis. In this work, we propose a novel end-to-end network for mammographic mass segmen…
Sparse Coding and Autoencoders
Akshay Rangamani, Anirbit Mukherjee, Amitabh Basu +4
In "Dictionary Learning" one tries to recover incoherent matrices (typically overcomplete and whose columns are assumed to be normalized) and spar…
Automatic Vertebra Labeling in Large-Scale 3D CT using Deep Image-to-Image Network with Message Passing and Sparsity Regularization
Dong Yang, Tao Xiong, Daguang Xu +10
Automatic localization and labeling of vertebra in 3D medical images plays an important role in many clinical tasks, including pathological diagnosis, surgical planning and postope…
Detecting Burnscar from Hyperspectral Imagery via Sparse Representation with Low-Rank Interference
Minh Dao, Xiang Xiang, Bulent Ayhan +2
In this paper, we propose a burnscar detection model for hyperspectral imaging (HSI) data. The proposed model contains two-processing steps in which the first step separate and the…
ICR: Iterative Convex Refinement for Sparse Signal Recovery Using Spike and Slab Priors
Hojjat S. Mousavi, Vishal Monga, Trac D. Tran
In this letter, we address sparse signal recovery using spike and slab priors. In particular, we focus on a Bayesian framework where sparsity is enforced on reconstruction coeffici…
Task-Driven Dictionary Learning for Hyperspectral Image Classification with Structured Sparsity Constraints
Xiaoxia Sun, Nasser M. Nasrabadi, Trac D. Tran
Sparse representation models a signal as a linear combination of a small number of dictionary atoms. As a generative model, it requires the dictionary to be highly redundant in ord…