Biography
Dr. Hassanpour’s research is focused on building novel machine learning and multimodal data analysis methods to inform precision health. His lab has been a pioneer in advancing digital pathology through deep learning methodologies. He has led multiple NIH-funded research projects on developing new machine learning models for medical image analysis and clinical text mining to improve diagnosis, prognosis, and personalized therapies. His research has resulted in numerous publications, software, and datasets that are widely recognized and received multiple awards, including the 2019 Agilent Early Career Professor Award for breakthroughs in digital pathology. Before joining Dartmouth, he worked as a Research Engineer at Microsoft. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and completed his postdoctoral training at Stanford Center for Artificial Intelligence in Medicine & Imaging.

Talk
AI for Digital Pathology
With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, digital pathology is primed to benefit dramatically from AI. This talk will cover several clinical applications of AI for characterizing histopathological patterns on high-resolution microscopy images for the classification, prognosis, and treatment of cancerous and precancerous lesions.