Multi-scale convolutional neural networks for lung nodule classification

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

Volumn 9123, Issue , 2015, Pages 588-599

  Shen, Wei ^a   Zhou, Mu ^c   Yang, Feng ^b   Yang, Caiyun ^a   Tian, Jie ^a  

^a INSTITUTE OF AUTOMATION   (China)

^b BEIJING JIAOTONG UNIVERSITY   (China)

^c UNIVERSITY OF SOUTH FLORIDA   (United States)

Author keywords

Computed tomography (CT) imaging; Computer aided diagnoses (CAD); Convolutional neural networks; Lung nodule classification

Indexed keywords

BIOLOGICAL ORGANS; CONVOLUTION; DIAGNOSIS; MEDICAL IMAGING; NEURAL NETWORKS; REGIONAL PLANNING; TOMOGRAPHY;

CLASS SPECIFIC FEATURES; COMPUTER AIDED DIAGNOSIS(CAD); CONVOLUTIONAL NEURAL NETWORK; DISCRIMINATIVE FEATURES; HIERARCHICAL LEARNING; LUNG NODULE; NODULE SEGMENTATION; REGIONAL ANALYSIS;

COMPUTERIZED TOMOGRAPHY;

ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; IMAGE QUALITY; LUNG NODULE; LUNG TUMOR; PROCEDURES; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; THORAX RADIOGRAPHY;

ALGORITHMS; HUMANS; LUNG NEOPLASMS; NEURAL NETWORKS (COMPUTER); PATTERN RECOGNITION, AUTOMATED; RADIOGRAPHIC IMAGE ENHANCEMENT; RADIOGRAPHIC IMAGE INTERPRETATION, COMPUTER-ASSISTED; RADIOGRAPHY, THORACIC; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SOLITARY PULMONARY NODULE; TOMOGRAPHY, X-RAY COMPUTED;

EID: 84983670549     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-19992-4_46     Document Type: Conference Paper

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