Dr. Hassanpour’s research is focused on developing intelligent computational methods to capture and organize unstructured biomedical information in a structured format to advance translational research and clinical practice. Dr. Hassanpour’s research laboratory works on building novel machine learning, natural language processing, and image analysis methods to extract clinical and health-related insights from medical records and images to improve diagnosis, prognosis, and targeted therapies. Before joining Dartmouth, he worked as a Research Engineer at Microsoft on high-throughput semantic text analysis for Web search queries for more than two years. Dr. Hassanpour received his Ph.D. in Electrical Engineering with a minor in Biomedical Informatics from Stanford University and a Master of Math in Computer Science from the University of Waterloo in Canada.
Deep Learning For Histology Image Analysis
With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit significantly from deep learning technology. This talk will cover new deep-learning-based methods for pathology slide analysis which have shown promising results for histologic characterization of the slides.
In order to expedite clinical diagnostics and advance precision patient care, innovative developments in algorithm development and imaging sciences, combined with improved understanding of the complex biology of cancer is crucial. This session will cover various developments, needs, and opportunities of expedited clinical decision-making.