Scalable histopathological image analysis via active learning

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 369-376

  Zhu, Yan ^a   Zhang, Shaoting ^b   Liu, Wei ^c   Metaxas, Dimitris N ^a  

^a RUTGERS UNIVERSITY   (United States)

^b University of North Carolina at Charlotte   (United States)

^c IBM T J WATSON RESEARCH CENTER   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

IMAGE ANALYSIS;

ACTIVE LEARNING METHODS; HISTOPATHOLOGICAL IMAGE ANALYSIS; HISTOPATHOLOGICAL IMAGES; INDEPENDENTLY AND IDENTICALLY DISTRIBUTED; MAXIMIZATION PROBLEM; SIMPLIFYING ASSUMPTIONS; STRUCTURED INFORMATION; SUBMODULAR FUNCTIONS;

ARTIFICIAL INTELLIGENCE;

ALGORITHM; ARTICLE; ARTIFICIAL INTELLIGENCE; AUTOMATED PATTERN RECOGNITION; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; FEMALE; HUMAN; IMAGE ENHANCEMENT; METHODOLOGY; MICROSCOPY; PATHOLOGY; REPRODUCIBILITY; SENSITIVITY AND SPECIFICITY;

ALGORITHMS; ARTIFICIAL INTELLIGENCE; BREAST NEOPLASMS; FEMALE; HUMANS; IMAGE ENHANCEMENT; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MICROSCOPY; PATTERN RECOGNITION, AUTOMATED; REPRODUCIBILITY OF RESULTS; SENSITIVITY AND SPECIFICITY;

EID: 84906985867     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-319-10443-0_47     Document Type: Conference Paper

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