University of Pittsburgh

AI Meets Cytology

Director of Machine Learning at UPMC Enterprises.
Date: 
Friday, November 2, 2018 - 12:30pm - 1:30pm

Bladder cancer is the sixth most common cancer in the United States with more than 80,000 new cases in 2018. Early detection by urine cytology greatly improves intervention success, but review of cytological slides is challenging: pathologists have to search for 10 malignant cells in glass slide specimens that can range from 100,000 to more than 300,000 cells. Many efforts are underway to optimize this procedure with digital whole slide imaging workflows, but limited attention has so far been given to automating cytology with artificial intelligence. We built a multi-level model based on clinically-relevant cellular features, strategically combining traditional machine learning and deep learning. The model demonstrates high accuracy at identifying malignant cells, as well as high accuracy at predicting the whole slide cytology diagnosis. In this talk we will describe our approach to model development, discuss the main challenges we faced, and review results on a held-out validation set.

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