Automatic Diagnosis of Ovarian Carcinomas via Sparse Multiresolution Tissue Representation

It has now been convincingly demonstrated that ovarian carcinoma subtypes are not a single disease but comprise a heterogeneous group of neoplasms. Whole slide images of tissue sections are used clinically for diagnosing biologically distinct subtypes, as opposed to different grades of the same disease. This new grading scheme for ovarian carcinomas results in a low to moderate interobserver agreement among pathologists. In practice, the majority of cases are diagnosed at advanced stages and the overall prognosis is typically poor. In this work, we propose an automatic system for the diagnosis of ovarian carcinoma subtypes from large-scale histopathology images. Our novel approach uses an unsupervised feature learning framework composed of a sparse tissue representation and a discriminative feature encoding scheme. We validate our model on a challenging clinical dataset of 80 patients and demonstrate its ability to diagnose whole slide images with an average accuracy of 91% using a linear support vector machine classifier.