Educational Objectives:
1. Provide an overview of the current state of digital imaging in cytology
2. Review the pros and cons of digital cytopathology and discuss the potential for emerging digital imaging applications
3. Understand the concept of quantitative histocytometry
4. Understand the use and application of multispectral imaging to cytometric analysis
5. Understand the concept of image classification using high dimensional data from fused data sources

In the first part of the session, we will discuss the digital imaging process, whole slide imaging, telecytology, imaging problems that are not specific to cytology, digital imaging problems unique to cytology, and emerging digital pathology applications. In the second part, we will focus on specific applications of digital pathology to evaluate, quantitate, and classify diseases, proteins, and outcomes from routine surgical pathology materials. Demonstrations will include a multi-parametric platform for quantitative analysis of tissues/cells simultaneously stained with multiple antibodies using multi-spectral imaging and image analysis algorithms. We will discuss how computers can be used for machine classification of disease states that recapitulates common grading schema in breast and prostate cancers as well as how machine learning can be used to develop new classifiers to predict disease outcomes in breast and prostate carcinoma.