Computer Aided Histopathological Classification of Cancer Subtypes

In this paper we present the results of our effort to develop a computer aided diagnosis system for pathological imaging data using renal cell carcinoma as a case study. Traditionally, cancer diagnosis is performed by an expert pathologist studying biopsy tissue under a microscope. Due to the complex nature of the task and the heterogeneity of patient tissue, these methods are not only time consuming but also suffer from subjective variability. To improve the repeatability and accuracy of the diagnosis process, a computational diagnosis system is proposed here. In this paper we report that with our novel knowledge-based methodology, we are able to achieve high level of classification accuracy (98%) when trying to classify 64 images (n=64) using a simple Bayesian classifier based on 8 extracted features and complete-leave-one-out cross-validation. This methodology is implemented in MATLAB and is expected to aid pathologists in the clinical setting to diagnose renal cell carcinoma as well as other types of cancer.