An automatic learning-based framework for robust nucleus segmentation

IEEE Transactions on Medical Imaging

Volumn 35, Issue 2, 2016, Pages 550-566

  Xing, Fuyong ^a   Xie, Yuanpu ^a   Yang, Lin ^a  

^a University of Florida   (United States)

Author keywords

Deep convolutional neural network; Nucleus segmentation; Sparse representation

Indexed keywords

COMPLEX NETWORKS; COMPUTER AIDED ANALYSIS; CONVOLUTION; ITERATIVE METHODS; MEDICAL IMAGING; NEURAL NETWORKS; TUMORS;

COMPARATIVE EXPERIMENTS; CONVOLUTIONAL NEURAL NETWORK; MORPHOLOGICAL FEATURES; NUCLEUS SEGMENTATION; PANCREATIC NEUROENDOCRINE TUMORS; REGION MERGING APPROACH; SEGMENTATION ALGORITHMS; SPARSE REPRESENTATION;

IMAGE SEGMENTATION;

ALGORITHM; ARTICLE; AUTOMATION; BRAIN NUCLEUS; BRAIN TUMOR; BREAST CANCER; CLINICAL FEATURE; DEEP CONVOLUTIONAL NEURAL NETWORK; GOLD STANDARD; HISTOPATHOLOGY; HUMAN; IMAGE ANALYSIS; LEARNING; NERVE CELL NETWORK; NUCLEUS SEGMENTATION; PROBABILITY; TASK PERFORMANCE; ARTIFICIAL NEURAL NETWORK; BRAIN; BREAST; BREAST TUMOR; CELL NUCLEUS; DIAGNOSTIC IMAGING; FEMALE; IMAGE PROCESSING; PATHOLOGY; PROCEDURES;

ALGORITHMS; BRAIN; BRAIN NEOPLASMS; BREAST; BREAST NEOPLASMS; CELL NUCLEUS; FEMALE; HUMANS; IMAGE PROCESSING, COMPUTER-ASSISTED; NEURAL NETWORKS (COMPUTER);

EID: 84959431409     PISSN: 02780062     EISSN: 1558254X     Source Type: Journal    
DOI: 10.1109/TMI.2015.2481436     Document Type: Article

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