Unsupervised learning with independent component analysis can identify patterns of glaucomatous visual field defects

Transactions of the American Ophthalmological Society

Volumn 103, Issue , 2005, Pages 270-280

  Goldbaum, Michael Henry ^a,b  

^a UNIVERSITY OF CALIFORNIA   (United States)

^b VA SAN DIEGO HEALTHCARE SYSTEM   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTICLE; DISEASE SEVERITY; GLAUCOMA; HUMAN; INDEPENDENT COMPONENT ANALYSIS; LEARNING; OPTIC NERVE DISEASE; PRIORITY JOURNAL; VISUAL FIELD; VISUAL FIELD DEFECT;

ARTIFICIAL INTELLIGENCE; BAYES THEOREM; DIAGNOSIS, COMPUTER-ASSISTED; GLAUCOMA; HUMANS; LEARNING; OPTIC NERVE DISEASES; PERIMETRY; SEVERITY OF ILLNESS INDEX; VISION DISORDERS; VISUAL FIELD TESTS; VISUAL FIELDS;

EID: 29944445992     PISSN: 15456110     EISSN: 15456110     Source Type: Journal    
DOI: None     Document Type: Article

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