D. Shen

Publications
Influence

Multimodal classification of Alzheimer's disease and mild cognitive impairment

D. Zhang, Yaping Wang, L. Zhou, H. Yuan, D. Shen
Psychology, Computer Science
NeuroImage
1 April 2011

We propose to combine three modalities of biomarkers, i.e., MRI, FDG-PET, and CSF, to discriminate between AD (or MCI) and healthy controls, using a kernel combination method. Expand

Deep convolutional neural networks for multi-modality isointense infant brain image segmentation

W. Zhang, R. Li, +4 authors D. Shen
Computer Science, Medicine
NeuroImage
1 March 2015

The segmentation of infant brain tissue images into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) plays an important role in studying early brain development in health and disease. Expand

Enriched white matter connectivity networks for accurate identification of MCI patients

Chong-Yaw Wee, Pew-Thian Yap, +6 authors D. Shen
Psychology, Medicine
NeuroImage
1 February 2011

We propose an effective network-based multivariate classification algorithm, using a collection of measures derived from white matter (WM) connectivity networks, to accurately identify MCI patients from normal controls. Expand

Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis

H. Suk, Seong-Whan Lee, D. Shen
Computer Science, Medicine
NeuroImage
1 November 2014

We propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Expand

Medical Image Synthesis with Context-Aware Generative Adversarial Networks

Dong Nie, Roger Trullo, C. Petitjean, S. Ruan, D. Shen
Computer Science, Medicine
MICCAI
16 December 2016

We train a fully convolutional network (FCN) to generate CT images from MR images, and also outperforms three state-of-the-art methods under comparison. Expand

Identification of MCI individuals using structural and functional connectivity networks

Chong-Yaw Wee, Pew-Thian Yap, +6 authors D. Shen
Psychology, Medicine
NeuroImage
1 February 2012

This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). Expand

Latent feature representation with stacked auto-encoder for AD/MCI diagnosis

H. Suk, Seong-Whan Lee, D. Shen, The Alzheimer’s Disease Neuroimaging Initiative
Psychology, Medicine
Brain Structure and Function
1 March 2015

Recently, there have been great interests for computer-aided diagnosis of Alzheimer’s disease (AD) and its prodromal stage, mild cognitive impairment (MCI). Unlike the previous methods that… Expand

Lung Infection Quantification of COVID-19 in CT Images with Deep Learning

F. Shan, Yaozong Gao, +6 authors Yuxin Shi
Computer Science, Engineering
ArXiv
10 March 2020

A deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios. Expand

Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19

F. Shi, J. Wang, +6 authors D. Shen
Computer Science, Medicine
IEEE reviews in biomedical engineering
6 April 2020

The pandemic of coronavirus disease 2019 (COVID-19) is spreading all over the world. Expand

Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients

Chong-Yaw Wee, Pew-Thian Yap, +5 authors D. Shen
Computer Science, Medicine
PloS one
30 May 2012

In this paper, a high-dimensional pattern classification framework, based on functional associations between brain regions during resting-state, is proposed to accurately identify MCI individuals from subjects who experience normal aging. Expand

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