Event Details:
Talk 1: Towards Uderstanding 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.
About:
Anthony Gatti, PhD: Postdoctoral Research Fellow, Department of Radiology
Talk 2: 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.
About:
Liangqiong Qu, PhD: Postdoctoral Research Fellow, Department of Biomedical Data Sciences