Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features

Neurocomputing

Volumn 229, Issue , 2017, Pages 34-44

  Wan, Tao ^a   Cao, Jiajia ^a   Chen, Jianhui ^b   Qin, Zengchang ^a  

^a BEIHANG UNIVERSITY   (China)

^b No 91 Central Hospital of PLA   (China)

Author keywords

Breast cancer grading; Cascaded ensemble; Convolutional neural networks; Histopathology; Multi level features

Indexed keywords

BIOPSY; CONVOLUTION; DISEASES; GRADING; IMAGE ENHANCEMENT; IMAGE SEGMENTATION; NEURAL NETWORKS; PIXELS; SEMANTICS; SUPPORT VECTOR MACHINES;

BREAST CANCER GRADING; CASCADED ENSEMBLE; CONVOLUTIONAL NEURAL NETWORK; HISTOPATHOLOGY; MULTILEVELS;

MEDICAL IMAGING;

EOSIN; HEMATOXYLIN;

AREA UNDER THE CURVE; ARTICLE; ARTIFICIAL NEURAL NETWORK; AUTOMATION; BREAST BIOPSY; BREAST CANCER; CANCER GRADING; CANCER MORPHOLOGY; CELL NUCLEUS; CONVOLUTIONAL NEURAL NETWORK; DATA EXTRACTION; DIAGNOSTIC ACCURACY; DIAGNOSTIC TEST ACCURACY STUDY; FALSE NEGATIVE RESULT; FALSE POSITIVE RESULT; FEMALE; HISTOPATHOLOGY; HUMAN; HUMAN TISSUE; IMAGE ANALYSIS; IMAGE SEGMENTATION; MAJOR CLINICAL STUDY; MATHEMATICAL COMPUTING; PERFORMANCE; RECEIVER OPERATING CHARACTERISTIC; SEMANTICS; SENSITIVITY AND SPECIFICITY; STAINING; STRUCTURE ANALYSIS; SUPPORT VECTOR MACHINE;

EID: 85006455969     PISSN: 09252312     EISSN: 18728286     Source Type: Journal    
DOI: 10.1016/j.neucom.2016.05.084     Document Type: Article

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