Feature Importance in Nonlinear Embeddings (FINE): Applications in Digital Pathology

IEEE Transactions on Medical Imaging

Volumn 35, Issue 1, 2016, Pages 76-88

  Ginsburg, Shoshana B ^a   Lee, George ^a   Ali, Sahirzeeshan ^a   Madabhushi, Anant ^a  

^a Case Western Reserve University   (United States)

Author keywords

Digital pathology; Dimensionality reduction; Feature selection; Terms Quantitative histomorphometry

Indexed keywords

DISEASES; FEATURE EXTRACTION; FORECASTING; PATHOLOGY; PRINCIPAL COMPONENT ANALYSIS; UROLOGY;

CLASSIFICATION ACCURACY; DIGITAL PATHOLOGIES; DIMENSIONALITY REDUCTION; FEATURE SCORING METHODS; HIGH-DIMENSIONAL FEATURE SPACE; HISTOMORPHOMETRY; PRINCIPAL COMPONENTS ANALYSIS; PROSTATE CANCER RECURRENCE;

CLASSIFICATION (OF INFORMATION);

ARTICLE; BIOCHEMICAL RECURRENCE; BREAST CANCER; CANCER RECURRENCE; EMBEDDING; FEATURE IMPORTANCE IN NONLINEAR EMBEDDING; MORPHOMETRICS; PRINCIPAL COMPONENT ANALYSIS; PROSTATE CANCER; TISSUE MICROARRAY; ALGORITHM; BIOPSY; BREAST TUMOR; COMPUTER ASSISTED DIAGNOSIS; CYTOCHEMISTRY; DIAGNOSTIC IMAGING; FEMALE; HUMAN; MALE; NONLINEAR SYSTEM; PATHOLOGY; PROCEDURES; PROSTATE TUMOR; RECURRENT DISEASE;

ALGORITHMS; BIOPSY; BREAST NEOPLASMS; DIAGNOSTIC IMAGING; FEMALE; HISTOCYTOCHEMISTRY; HUMANS; IMAGE INTERPRETATION, COMPUTER-ASSISTED; MALE; NONLINEAR DYNAMICS; PATHOLOGY; PROSTATIC NEOPLASMS; RECURRENCE;

EID: 84959353019     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2456188     Document Type: Article

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