Computational Pathology

Our research develops computational approaches to analyze data generated in the pathology lab. Our goal is to improve diagnostics, to advance clinical translation of computational pathology research, and to provide investigators with tools to generate new insights from complex data. To accomplish these goals we focus on:

1. Fundamental research in machine-learning and artificial intelligence
2. Development of software infrastructure for computational pathology
3. Generating annotated datasets for training and validation of computational pathology algorithms

We apply these techniques to a number of problems including:

1. Measuring immune response in cancer and development of immuno-oncology biomarkers
2. Prediction of clinical outcomes from genomic and digital pathology data
3. Classification of hematologic malignancies