Integrated diagnostics: a conceptual framework with examples
| Autor: | Michael Feldman, James Monaco, Anant Madabhushi, Ajay Basavanhally, Scott Doyle, Steve Masters, George Lee, John E. Tomaszewski |
|---|---|
| Rok vydání: | 2010 |
| Předmět: |
Male
Pathology medicine.medical_specialty Receptor ErbB-2 Clinical Biochemistry Decision tree Breast Neoplasms Machine learning computer.software_genre Pattern Recognition Automated medicine Humans Diagnosis Computer-Assisted Face detection Pathology Clinical business.industry Biochemistry (medical) Nonlinear dimensionality reduction Prostatic Neoplasms Digital pathology General Medicine Sensor fusion Conceptual framework Computer-aided diagnosis Pattern recognition (psychology) Female Artificial intelligence business computer Algorithms |
| Zdroj: | cclm. 48:989-998 |
| ISSN: | 1437-4331 1434-6621 |
| DOI: | 10.1515/cclm.2010.193 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|