Machine Learning in Medicine (MLxMed) Seminar: Saeed Hassanpour, Professor, Dartmouth

Talk title: Advancing Precision Medicine through Machine Learning in Computational Pathology

Talk abstract: With the recent expansions of whole-slide digital scanning, archiving, and high-throughput tissue banks, the field of digital pathology is primed to benefit dramatically from deep learning technology. This talk will cover several clinical applications of deep learning for characterizing histopathological patterns on high-resolution microscopy images for the classification, prognosis, and treatment of cancerous and precancerous lesions. Furthermore, the current practical challenges of building deep learning models for pathology image analysis will be discussed and new methodological advances to address these bottlenecks will be presented. The internal and external evaluation results show these approaches' high accuracy and generalizability across different cancer and lesion types, data sources, and slide types. These results demonstrate these novel methods could have a significant impact on the current standard of patient care by assisting pathologists in clinical practice, improving access, efficiency, and accuracy of the histopathological interpretation, and providing a framework for integrating histopathological features with other clinical, imaging, and molecular information for the comprehensive modeling of patient outcomes and promoting precision medicine.

When 2:00 pm to 3:00 pm on Friday, April 5, 2024
Location Zoom
Fees Free
Speakers Saeed Hassanpour, PhD, Professor, Departments of Biomedical Data Science, Computer Science, and Epidemiology, Dartmouth University