Incremental learning with selective memory (ILSM): Towards fast prostate localization for image guided radiotherapy

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

Volumn 8150 LNCS, Issue PART 2, 2013, Pages 378-386

  Gao, Yaozong ^a,b   Zhan, Yiqiang ^c   Shen, Dinggang ^a  

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

^b University of North Carolina   (United States)

^c SIEMENS CORPORATE RESEARCH   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ANATOMICAL VARIATIONS; APPEARANCE MODELING; IMAGE GUIDED RADIOTHERAPY; INCREMENTAL LEARNING; LEARNING FRAMEWORKS; LOCALIZATION ACCURACY; PATIENT SPECIFIC; PROSTATE LOCALIZATION;

COMPUTERIZED TOMOGRAPHY; RADIOTHERAPY;

UROLOGY;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; COMPUTER ASSISTED DIAGNOSIS; COMPUTER ASSISTED RADIOTHERAPY; HUMAN; IMAGE ENHANCEMENT; IMAGE GUIDED RADIOTHERAPY; MALE; METHODOLOGY; PROSTATE TUMOR; RADIOGRAPHY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; PATTERN RECOGNITION, AUTOMATED; PROSTATIC NEOPLASMS; RADIOTHERAPY, CONFORMAL; RADIOTHERAPY, IMAGE-GUIDED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84885912483     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-642-40763-5_47     Document Type: Conference Paper

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