Machine Learning in Medicine (MLxMed) Saeed Hassanpour, Dartmouth University, April 5th
A Virtual Seminar Series
Hosted by the Department of Biomedical Informatics, University of Pittsburgh; the Hariri Institute for Computing, Boston University; and the University of Toronto
Friday, April 5, 2024
2:00 PM – 3:00 PM Eastern Time
Zoom https://pitt.zoom.us/j/95267258914 (Details are listed at the end)
Speaker: Saeed Hassanpour, PhD, Professor, Departments of Biomedical Data Science, Computer Science, and Epidemiology, Dartmouth University
Talk Title: Advancing Precision Medicine through Machine Learning in Computational Pathology
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.
Faculty Host: Kayhan Batmanghelich, Assistant Professor (ECE)
About MLxMed Seminar Series: Medicine is complex and data-driven, while discovery and decision-making are increasingly enabled by machine learning. Machine learning has the potential to support, enable and improve medical discovery and clinical decision making in areas such as medical imaging, cancer diagnostics, precision medicine, clinical trials, and electronic health records. This seminar series focuses on new algorithms, real-world deployment, and future trends in machine learning in medicine. It will feature prominent investigators who are developing and applying machine learning to biomedical discovery and in clinical decision support. For more information, see MLxMed website: (http://ml-in-medicine.org/)
Zoom Information
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Webinar ID: 952 6725 8914
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