Simultaneous sparsity model for histopathological image representation and classification

IEEE Transactions on Medical Imaging

Volumn 33, Issue 5, 2014, Pages 1163-1179

  Srinivas, Umamahesh ^a   Mousavi, Hojjat Seyed ^a   Monga, Vishal ^a   Hattel, Arthur ^b   Jayarao, Bhushan ^b  

^a The Pennsylvania State University   (United States)

^b PENNSYLVANIA STATE UNIVERSITY   (United States)

Author keywords

Classification; histopathological image analysis; multichannel images; sparse representation

Indexed keywords

CLASSIFICATION (OF INFORMATION); MAMMALS;

CLASSIFICATION ACCURACY; HISTOPATHOLOGICAL IMAGE ANALYSIS; HISTOPATHOLOGICAL IMAGES; MULTICHANNEL IMAGES; OPTIMIZATION PROBLEMS; PENNSYLVANIA STATE UNIVERSITY; REAL-WORLD IMAGE DATA; SPARSE REPRESENTATION;

IMAGE CLASSIFICATION;

ARTICLE; BREAST DYSPLASIA; BREAST LESION; DIAGNOSTIC ACCURACY; DISEASE CLASSIFICATION; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; INTRADUCTAL CARCINOMA; PATHOLOGIST; ALGORITHM; ANIMAL; BREAST; BREAST TUMOR; CYTOCHEMISTRY; FACTUAL DATABASE; FEMALE; IMAGE PROCESSING; KIDNEY; PATHOLOGY; PROCEDURES; RECEIVER OPERATING CHARACTERISTIC; SPLEEN;

ALGORITHMS; ANIMALS; BREAST; BREAST NEOPLASMS; DATABASES, FACTUAL; FEMALE; HISTOCYTOCHEMISTRY; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; KIDNEY; ROC CURVE; SPLEEN;

EID: 84899714454     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2014.2306173     Document Type: Article

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