Journal
CLINICAL CHEMISTRY AND LABORATORY MEDICINE
Volume 48, Issue 7, Pages 989-998Publisher
WALTER DE GRUYTER GMBH
DOI: 10.1515/CCLM.2010.193
Keywords
diagnostic pathology; digitalized histopathology; image analysis algorithms; pattern recognition; proteomic signature; theragnostic
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Funding
- Wallace H. Coulter Foundation
- New Jersey Commission on Cancer Research
- National Cancer Institute [R01CA136535-01, R21CA127186, R03CA128081-01, R03CA143991-01]
- Cancer Institute of New Jersey
- Rutgers University
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With the advent of digital pathology, imaging scientists have begun to develop computerized image analysis algorithms for making diagnostic (disease presence), prognostic (outcome prediction), and theragnostic (choice of therapy) predictions from high resolution images of digitized histopathology. One of the caveats to developing image analysis algorithms for digitized histopathology is the ability to deal with highly dense, information rich datasets; datasets that would overwhelm most computer vision and image processing algorithms. Over the last decade, manifold learning and non-linear dimensionality reduction schemes have emerged as popular and powerful machine learning tools for pattern recognition problems. However, these techniques have thus far been applied primarily to classification and analysis of computer vision problems (e. g., face detection). In this paper, we discuss recent work by a few groups in the application of manifold learning methods to problems in computer aided diagnosis, prognosis, and theragnosis of digitized histopathology. In addition, we discuss some exciting recent developments in the application of these methods for multi-modal data fusion and classification; specifically the building of meta-classifiers by fusion of histological image and proteomic signatures for prostate cancer outcome prediction. Clin Chem Lab Med 2010; 48: 989-98.
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