Baris Turkbey, MD, FSAR
Section Chief of MRI
Section Chief of Artificial Intelligence
Molecular Imaging Branch
National Cancer Institute, NIH
Title: Advanced Prostate Cancer Imaging
- To discuss current status and limitations of localized prostate cancer diagnosis.
- To discuss use of artificial intelligence in diagnosis of localized prostate cancer.
- To discuss use of molecular imaging in clinical prostate cancer management.
Dr. Turkbey obtained his medical degree from Hacettepe University in Ankara, Turkey in 2003. He completed his residency in Diagnostic and Interventional Radiology at Hacettepe University. He joined Molecular Imaging Branch (MIB), National Cancer Institute, NIH in 2007. His main research areas are imaging of prostate cancer (multiparametric MRI, PET CT), image guided biopsy and treatment techniques (focal therapy, surgery and radiation therapy) for prostate cancer and artificial intelligence. Dr. Turkbey is a member of Prostate Imaging Reporting & Data System (PI-RADS) Steering Committee. He is the Director Magnetic Resonance Imaging section in MIB and the Artificial Intelligence Resource in MIB.
In Person at the Clark Center S360 – Lunch will be provided!
Anthony Gatti, PhD
Postdoctoral Research Fellow
Department of Radiology
Wu Tsai Human Performance Alliance
Title: Towards Understanding Knee Health Using Automated MRI-Based Statistical Shape Models
Abstract: Knee injuries and pain are prevalent across all ages, with varying causes from “anterior knee pain” in runners to osteoarthritis-related pain. Osteoarthritis pain is a particular problem because structural outcomes assessed on medical images often disagree with symptoms. Most studies trying to understand knee health and pain use simple biomarkers such as mean cartilage thickness. My talk will present an automated pipeline for quantifying the whole knee using statistical shape modeling. I will present a conventional statistical shape model as well as a novel approach that uses generative neural implicit representations. Both modeling approaches allow unsupervised identification of salient anatomic features. I will demonstrate how these features can be used to predict existing radiographic outcomes, patient demographics, and knee pain.
Liangqiong Qu, PhD
Postdoctoral Research Fellow
Department of Biomedical Data Sciences
Title: Distributed Deep Learning in Medical Imaging
Abstract: Distributed deep learning is an emerging research paradigm for enabling collaboratively training deep learning models without sharing patient data.
In this talk, we will first investigate the use distributed deep learning to build medical imaging classification models in a real-world collaborative setting.
We then present several strategies to tackle the data heterogeneity challenge and the lack of quality labeled data challenge in distributed deep learning.