A MAP approach for convex non-negative matrix factorization in the diagnosis of brain tumors
A Vilamala, LA Belanche… - … International Workshop on …, 2014 - ieeexplore.ieee.org
2014 International Workshop on Pattern Recognition in Neuroimaging, 2014•ieeexplore.ieee.org
Convex non-negative matrix factorization is a blind signal separation technique that has
previously demonstrated to be well-suited for the task of human brain tumor diagnosis from
magnetic resonance spectroscopy data. This is due to its ability to retrieve interpretable
sources of mixed sign that highly correlate with tissue type prototypes. The current study
provides a Bayesian formulation for such problem and derives a maximum a posteriori
estimate based on a gradient descent algorithm specifically designed to deal with matrices …
previously demonstrated to be well-suited for the task of human brain tumor diagnosis from
magnetic resonance spectroscopy data. This is due to its ability to retrieve interpretable
sources of mixed sign that highly correlate with tissue type prototypes. The current study
provides a Bayesian formulation for such problem and derives a maximum a posteriori
estimate based on a gradient descent algorithm specifically designed to deal with matrices …
Convex non-negative matrix factorization is a blind signal separation technique that has previously demonstrated to be well-suited for the task of human brain tumor diagnosis from magnetic resonance spectroscopy data. This is due to its ability to retrieve interpretable sources of mixed sign that highly correlate with tissue type prototypes. The current study provides a Bayesian formulation for such problem and derives a maximum a posteriori estimate based on a gradient descent algorithm specifically designed to deal with matrices with different sign restrictions. Its applicability to neuro-oncology diagnosis was experimentally assessed and the results were found to be comparable to those achieved by state of the art methods in tumor type discrimination and consistently better in source extraction.
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