title: Semi-supervised pattern classification of medical images: application to mild cognitive impairment (MCI).
abstract: Many progressive disorders are characterized by unclear or transient diagnoses for specific subgroups of patients. Commonly used supervised pattern recognition methodology may not be the most suitable approach to deriving image-based biomarkers in such cases, as it relies on the availability of categorically labeled data (e.g., patients and controls). In this paper, we explore the potential of semi-supervised pattern classification to provide image-based biomarkers in the absence of precise diagnostic information for some individuals. We employ semi-supervised support vector machines (SVM) and apply them to the problem of classifying MR brain images of patients with uncertain diagnoses. We examine patterns in serial scans of ADNI participants with mild cognitive impairment (MCI), and propose that in the absence of sufficient follow-up evaluations of individuals with MCI, semi-supervised strategy is potentially more appropriate than the fully-supervised paradigm employed up to date.
authors.affiliation: Section of Biomedical Image Analysis, Department of Radiology, University of Pennsylvania, 3600 Market St., Suite 380, Philadelphia, PA 19104, USA. roman.filipovych@uphs.upenn.edu
authors.forename: Roman
authors.initials: R
authors.lastname: Filipovych
authors.forename: Christos
authors.initials: C
authors.lastname: Davatzikos
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D019540:Area Under Curve; D001185:Artificial Intelligence; D001284:Atrophy; D015415:Biomarkers; D001921:Brain / Q000473:pathology; D003072:Cognition Disorders / Q000145:classification* / Q000473:pathology*; D003952:Diagnostic Imaging / Q000145:classification*; D018450:Disease Progression; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D010363:Pattern Recognition, Automated
chemical_list: D015415:Biomarkers