IBIIS-AIMI Seminar: Using AI for Longitudinal Tumor Response Monitoring and AI Guided Cancer Treatments: From Lab to Clinic - Harini Veeraraghavan, PhD
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Abstract:
Cancer patients are imaged with multiple imaging modalities as part of routine cancer care. However, the rich information available from the images are not fully exploited to better manage patient care through earlier intervention as well as more precise targeted treatments. In this talk, I will present some of the new AI methodologies we have been developing to track tumor response as well as from routinely acquired imaging applied to image-guided radiation treatments using CT/cone-beam CT as well as MRI-guided precision treatments. I will also present some demonstration studies of how AI based automated segmentation and tumor as well as healthy tissue change assessment can be used to early detect treatment toxicities to enable clinicians to better manage cancer care. Finally, I will show how these developed methods have been put to routine clinical care for automating radiotherapy treatment planning at MSK.
About:
Harini Veeraraghavan is an Associate Attending Computer Scientist in the Department of Medical Physics at Memorial Sloan Kettering Cancer Center, NY. Her research interests are in the tumor treatment response prediction and longitudinal treatment monitoring using radiomics and advanced AI and image analysis methods applied to radiological images as well as integrating multi-modality information for early response prediction. She also leads and directs the clinical translation of AI methods developed by her group for radiotherapy treatment automation at Memorial Sloan Kettering. Prior to working at MSK, she as a computer vision scientist at General Electric Research. She received her PhD in computer science from University of Minnesota and was at Carnegie Mellon University as a postdoctoral researcher developing methods for human robot interaction based learning methodologies applied to humanoid robots.
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