Imaging Science Seminar: Foundation Models and Their Potential Role in Future Cancer Care

Imaging Science Seminar
Foundation Models and Their Potential Role in Future Cancer Care

Dr. Ghulam Rasool
Assistant Member, Department of Machine Learning
H. Lee Moffitt Cancer Center and Research Institute

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Abstract:
The recent advancements in generative artificial intelligence (AI) related to natural language processing (NLP), computer vision (CV), and multimodal learning have led to the development of Foundation Models (FMs). Examples of FMs include large language models (LLMs), vision-language models (VLMs), and multimodal models (MLMs). These models have fundamentally transformed our approach to processing structured, unstructured, image, categorical, time-series, and tabular data for predicting future variables of interest or extracting actionable insights. My lab, established two years ago at the newly founded Department of Machine Learning at Moffitt Cancer Center, focuses on leveraging FMs (including LLMs, VLMs, and MLMs) to tackle challenging problems in cancer care, such as cancer screening, early and accurate diagnosis, personalized treatment planning, and effective surveillance during cancer survivorship. This talk will begin by introducing FMs and will later delve deeply into how these models can be leveraged to address the most pressing problems in cancer care.:

Speaker Bio:
Ghulam Rasool is an Assistant Member in the Department of Machine Learning with a secondary clinical appointment in the Department of Neuro-Oncology at the H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL. He holds an Assistant Professor position in the Department of Oncological Sciences and a courtesy faculty appointment in the Department of Electrical Engineering at the University of South Florida. He received a BS in Mechanical Engineering from the National University of Sciences and Technology (NUST), Pakistan, in 2000, an M.S. in Computer Engineering from the Center for Advanced Studies in Engineering (CASE), Pakistan, in 2010, and a Ph.D. in Systems Engineering from the University of Arkansas at Little Rock in 2014. He served as a postdoctoral fellow at the Rehabilitation Institute of Chicago and Northwestern University from 2014 to 2016. Before joining Moffitt, he was an Assistant Professor in the Department of Electrical and Computer Engineering at Rowan University. His current research focuses on building trustworthy multimodal machine learning and artificial intelligence models for cancer diagnosis, treatment planning, and risk assessment. His research efforts are funded by the National Science Foundation (NSF), the National Institutes of Health (NIH), and the Moffitt Cancer Center. Recently, he received the 2023 Junior Researcher Award in the Quantitative Sciences at Moffitt Cancer Center

Intended Audience:
All are welcome.

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