427 citations · 3.3k across the 77 of their papers we have counts for
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Can Deep Learning Outperform Modern Commercial CT Image Reconstruction Methods?
Hongming Shan, Atul Padole, Fatemeh Homayounieh +5
Commercial iterative reconstruction techniques on modern CT scanners target radiation dose reduction but there are lingering concerns over their impact on image appearance and low…
Super-resolution MRI through Deep Learning
Qing Lyu, Chenyu You, Hongming Shan +1
Magnetic resonance imaging (MRI) is extensively used for diagnosis and image-guided therapeutics. Due to hardware, physical and physiological limitations, acquisition of high-resol…
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping
Yiming Lei, Yukun Tian, Hongming Shan +3
A number of studies on lung nodule classification lack clinical/biological interpretations of the features extracted by convolutional neural network (CNN). The methods like class a…
A Synergized Pulsing-Imaging Network (SPIN)
Qing Lyu, Tao Xu, Hongming Shan +1
Currently, the deep neural network is the mainstream for machine learning, and being actively developed for biomedical imaging applications with an increasing emphasis on tomograph…
Structure-sensitive Multi-scale Deep Neural Network for Low-Dose CT Denoising
Chenyu You, Qingsong Yang, Hongming Shan +8
Computed tomography (CT) is a popular medical imaging modality in clinical applications. At the same time, the x-ray radiation dose associated with CT scans raises public concerns…
3D Convolutional Encoder-Decoder Network for Low-Dose CT via Transfer Learning from a 2D Trained Network
Hongming Shan, Yi Zhang, Qingsong Yang +5
Low-dose computed tomography (CT) has attracted a major attention in the medical imaging field, since CT-associated x-ray radiation carries health risks for patients. The reduction…
Shamap: Shape-based Manifold Learning
Fenglei Fan, Ziyu Su, Yueyang Teng +1
For manifold learning, it is assumed that high-dimensional sample/data points are embedded on a low-dimensional manifold. Usually, distances among samples are computed to capture a…