• Publications
  • Influence
Multimodal classification of Alzheimer's disease and mild cognitive impairment
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
  • 865
  • 83
  • PDF
Deep convolutional neural networks for multi-modality isointense infant brain image segmentation
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
  • 506
  • 28
Enriched white matter connectivity networks for accurate identification of MCI patients
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
  • 174
  • 23
  • PDF
Hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis
We propose a novel method for a high-level latent and shared feature representation from neuroimaging modalities via deep learning. Expand
  • 434
  • 21
  • PDF
Medical Image Synthesis with Context-Aware Generative Adversarial Networks
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
  • 349
  • 19
  • PDF
Identification of MCI individuals using structural and functional connectivity networks
This study focuses on integrating multiple imaging modalities to identify individuals at risk for mild cognitive impairment (MCI). Expand
  • 286
  • 17
  • PDF
Latent feature representation with stacked auto-encoder for AD/MCI diagnosis
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 thatExpand
  • 284
  • 16
  • PDF
Lung Infection Quantification of COVID-19 in CT Images with Deep Learning
A deep learning (DL)-based segmentation system is developed to automatically quantify infection regions of interest (ROIs) and their volumetric ratios. Expand
  • 186
  • 15
  • PDF
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
  • 211
  • 14
  • PDF
Resting-State Multi-Spectrum Functional Connectivity Networks for Identification of MCI Patients
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
  • 102
  • 12
  • PDF