Round Robin – AI PHI Affinity Group February Seminar

Topic

The February Affinity Group meeting with include a round robin of 10 minute presentations from four AI Precision Health Institute students. The meeting will be hosted on Gather (gather.town). Please RSVP to be sent the link on Friday. See all topics below.

Deep learning furthers the understanding of local distributions of fat and muscle on body shape and health using 3D surface scans

Presented by Lambert Leong

I am a PhD student in the Molecular Bioscience and Bio-Engineering (MBBE) department at the University of Hawaii. I received my Masters in computer science where I focused on high performance computing and simulation. My Bachelors degree is in biology and I have three years of work experience in the bio-tech industry developing an artificial cornea for transplant. My work with the Shepherd Research Lab focuses on breast imagining and the use of machine learning and artificial intelligence for cancer risk analysis and detection. More information about me, my projects and works can be found at lambertleong.com.

Can artificial intelligence derived ultrasound breast density provide comparable breast cancer risk estimates to density derived from mammograms

Presented by Dustin Valdez

I am a graduate student pursuing my Ph.D. in Nutrition at the University of Hawaii at Manoa. Currently, I am working on the Makawalu Project which aims to bring accessible breast cancer screening technologies combined with artificial intelligence to low resource areas of the Pacific. Previously, I worked on evaluating the effectiveness of the iBreastExam as an early detection tool for breast cancer. In my free time, I enjoy practicing taekwondo and taiko drumming.

Deep learning predicts all-cause mortality from longitudinal total-body DXA imaging

Presented by Yannik Glaser

I am a Computer Science Master’s student at the UH ICS department, hoping to also earn my PhD there afterwards. I work at the UH Machine Learning Lab as a research assistant, applying machine learning techniques to various problems from different disciplines. I’m particularly passionate about deep learning algorithms and their application to the natural sciences. My work with the Cancer Center focuses on applying deep learning to medical image analysis.

Artificial Intelligence Detects, Classifies, and Describes Lesions in Clinical Breast Ultrasound Images

Presented by Arianna Bunnell

I’m a PhD student in the Computer Science (ICS) department at the University of Hawai’i. I have Bachelors degrees in Statistics and Computer Science, and am passionate about the application of data science to healthcare. I currently work as a Research Assistant in the UH Machine Learning Lab, working in applied ML. My work with the Shepherd Research Lab focuses on applying deep learning to breast ultrasound imaging.

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Artificial Intelligence in Cancer Research – AI PHI Affinity Group

(First Friday of each month)

This group was formed to discuss the current trends and applications of artificial intelligence in cancer research and clinical practice. The group brings together AI researchers in a variety of fields (computer science, engineering, nutrition, epidemiology radiology, etc) with clinicians and advocates. Students, trainees and faculty with any or no background in AI are encouraged to attend. The goal is to foster collaborative interactions to solve problems in cancer that were thought to be unsolvable a decade ago before the broad use of deep learning and AI in medicine.

Contact: Dr. John Shepherd at johnshep@hawaii.edu or Peter Washington at pyw@hawaii.edu