Tree RE-weighted belief propagation using deep learning potentials for mass segmentation from mammograms

Proceedings - International Symposium on Biomedical Imaging

Volumn 2015-July, Issue , 2015, Pages 760-763

  Dhungel, Neeraj ^a   Carneiro, Gustavo ^a   Bradley, Andrew P ^b  

^a UNIVERSITY OF ADELAIDE   (Australia)

^b UNIVERSITY OF QUEENSLAND   (Australia)

Author keywords

Deep learning; Gaussian Mixture model; Mammograms; mass segmentation; tree re weighted belief propagation

Indexed keywords

FORESTRY; GAUSSIAN DISTRIBUTION; IMAGE SEGMENTATION; LEARNING SYSTEMS; MAMMOGRAPHY; MEDICAL IMAGING; RANDOM PROCESSES; X RAY SCREENS;

BELIEF PROPAGATION; CONDITIONAL RANDOM FIELD; GAUSSIAN MIXTURE MODEL; LEARNING MECHANISM; LEARNING POTENTIAL; MAMMOGRAMS; MASS SEGMENTATION; POTENTIAL FUNCTION;

DEEP LEARNING;

EID: 84944323200     PISSN: 19457928     EISSN: 19458452     Source Type: Conference Proceeding    
DOI: 10.1109/ISBI.2015.7163983     Document Type: Conference Paper

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