Tumor segmentation in whole slide images using persistent homology and deep convolutional features

Communications in Computer and Information Science

Volumn 723, Issue , 2017, Pages 320-329

  Qaiser, Talha ^a   Tsang, Yee Wah ^b   Epstein, David ^c   Rajpoot, Nasir ^a  

^a UNIVERSITY OF WARWICK   (United Kingdom)

^b UNIVERSITY HOSPITALS COVENTRY AND WARWICKSHIRE NHS TRUST   (United Kingdom)

^c UNIVERSITY OF WARWICK   (United Kingdom)

Author keywords

Deep learning; Digital pathology; Histology image analysis; Persistent homology; Tumor segmentation

Indexed keywords

CONVOLUTION; DECISION TREES; DEEP LEARNING; HISTOLOGY; IMAGE ANALYSIS; IMAGE UNDERSTANDING; MEDICAL IMAGING; REGRESSION ANALYSIS; TOPOLOGY; TUMORS;

CONVOLUTION NEURAL NETWORK; DIGITAL PATHOLOGIES; ENSEMBLE STRATEGIES; HISTOLOGY IMAGES; PERSISTENT HOMOLOGY; SEGMENTATION PERFORMANCE; TOPOLOGICAL FEATURES; TUMOR SEGMENTATION;

IMAGE SEGMENTATION;

EID: 85022223612     PISSN: 18650929     EISSN: None     Source Type: Book Series    
DOI: 10.1007/978-3-319-60964-5_28     Document Type: Conference Paper

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