Augmenting capsule endoscopy diagnosis: A similarity learning approach

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

Volumn 6362 LNCS, Issue PART 2, 2010, Pages 454-462

  Seshamani, S ^a   Kumar, R ^a   Dassopoulos, T ^c   Mullin, G ^b   Hager, G ^a  

^a Johns Hopkins University   (United States)

^b JOHNS HOPKINS HOSPITAL   (United States)

^c WASHINGTON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AUTOMATED METHODS; CAPSULE ENDOSCOPY; CROHN'S DISEASE; LEARNING APPROACH; LEARNING METHODS; LESION IMAGE; MANUAL PROCESS; TRAINING TIME; VIDEO SEQUENCES; VOTING SCHEMES;

CLASSIFIERS; ENDOSCOPY; MEDICAL COMPUTING; VIDEO RECORDING;

MEDICAL IMAGING;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; CAPSULE ENDOSCOPY; COMPUTER ASSISTED DIAGNOSIS; CROHN DISEASE; HUMAN; IMAGE ENHANCEMENT; IMAGE SUBTRACTION; METHODOLOGY; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; CAPSULE ENDOSCOPY; CROHN DISEASE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY; SUBTRACTION TECHNIQUE;

EID: 84866619997     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-15745-5_56     Document Type: Conference Paper

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