Automatic classification of pulmonary peri-fissural nodules in computed tomography using an ensemble of 2D views and a convolutional neural network out-of-the-box

Medical Image Analysis

Volumn 26, Issue 1, 2015, Pages 195-202

  Ciompi, Francesco ^a   de Hoop, Bartjan ^b   van Riel, Sarah J ^a   Chung, Kaman ^a   Scholten, Ernst Th ^a   Oudkerk, Matthijs ^c   de Jong, Pim A ^b   Prokop, Mathias ^a   van Ginneken, Bram ^a,d  

^a RADBOUD UNIVERSITY MEDICAL CENTER   (Netherlands)

^b UNIVERSITY MEDICAL CENTER UTRECHT   (Netherlands)

^c UNIVERSITY MEDICAL CENTER GRONINGEN   (Netherlands)

^d FRAUNHOFER MEVIS   (Germany)

Author keywords

Chest CT; Convolutional neural networks; Deep learning; Lung cancer screening; OverFeat; Peri fissural nodules

Indexed keywords

ARTIFICIAL INTELLIGENCE; CONVOLUTION; LEARNING SYSTEMS; NEURAL NETWORKS;

CHEST CT; CONVOLUTIONAL NEURAL NETWORK; DEEP LEARNING; LUNG CANCER SCREENING; OVERFEAT; PERI-FISSURAL NODULES;

COMPUTERIZED TOMOGRAPHY;

ARTICLE; AUTOMATION; CANCER CLASSIFICATION; CANCER SCREENING; CLASSIFIER; COMPUTER ASSISTED TOMOGRAPHY; HUMAN; LUNG CANCER; PRIORITY JOURNAL; PULMONARY PERI FISSURAL NODULE; ALGORITHM; ARTIFICIAL NEURAL NETWORK; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; LUNG NODULE; LUNG TUMOR; PROCEDURES; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY; SOFTWARE; THREE DIMENSIONAL IMAGING;

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

EID: 84943752367     PISSN: 13618415     EISSN: 13618423     Source Type: Journal    
DOI: 10.1016/j.media.2015.08.001     Document Type: Article

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