Calendar

Dec
14
Wed
2022
IBIIS & AIMI Hybrid Seminar: Anthony Gatti, PhD & Liangqiong Qu, PhD @ Clark Center S360
IBIIS & AIMI Hybrid Seminar: Anthony Gatti, PhD & Liangqiong Qu, PhD
Dec 14 @ 1:00 pm – 2:00 pm Clark Center S360

In Person at the Clark Center S360 – Lunch will be provided!
Zoom: https://stanford.zoom.us/j/99496515255?pwd=MHlXbXM2WXJULzZwemk1WjJHNFZOdz09

Anthony Gatti, PhD
Postdoctoral Research Fellow
Department of Radiology
Wu Tsai Human Performance Alliance
Stanford University

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
Stanford University

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.