Deep learning based imaging data completion for improved brain disease diagnosis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 8675 LNCS, Issue PART 3, 2014, Pages 305-312

  Li, Rongjian ^a   Zhang, Wenlu ^a   Suk, Heung Il ^b   Wang, Li ^b   Li, Jiang ^c   Shen, Dinggang ^b   Ji, Shuiwang ^a  

^a ECS Building   (United States)

^b UNIVERSITY OF NORTH CAROLINA SCHOOL OF MEDICINE   (United States)

^c OLD DOMINION UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

MAGNETIC RESONANCE IMAGING; NEURAL NETWORKS; NEUROIMAGING;

ALZHEIMER'S DISEASE; CONVOLUTIONAL NEURAL NETWORK; DISEASE DIAGNOSIS; INPUT AND OUTPUTS; INPUT MODALITIES; MULTI-MODALITY; MULTI-MODALITY IMAGING; OUTPUT MODALITY;

DIAGNOSIS;

ALGORITHM; ALZHEIMER DISEASE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; CONTROLLED CLINICAL TRIAL; CONTROLLED STUDY; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MULTIMODAL IMAGING; NUCLEAR MAGNETIC RESONANCE IMAGING; POSITRON EMISSION TOMOGRAPHY; RANDOMIZED CONTROLLED TRIAL; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ALZHEIMER DISEASE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MAGNETIC RESONANCE IMAGING; MULTIMODAL IMAGING; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; POSITRON-EMISSION TOMOGRAPHY; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84906979740     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10443-0_39     Document Type: Conference Paper

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