Inverse Problems Seminar Physics-Informed Personalization in Electrocardiographic Imaging via Machine Learning
Inverse Problems Seminar
Physics-Informed Personalization in Electrocardiographic Imaging via Machine Learning
Xiajun Jiang & Maryam Toloubidokhti
Ph. D. Candidates
Computing & Information Science Ph. D. Program, RIT
Register Here for Zoom Link
Abstract:
Incorporating patient-specific physics-informed knowledge as model parameters is important in electrocardiographic imaging (ECGI) models. Traditional approaches to ECGI are heavily physics-informed under the framework of state-space modeling (SSM), while the strong reliance on prior knowledge for model assumption precludes it from being identified from data only. Modern deep learning methods have no such reliance but are not good at disentangling the latent dynamics and its observation at data space and lack in interpret-ability. Our works present physics-informed personalized methods that 1) consider subject-specific geometry in the inverse mapping, 2) disentangle the emission model and latent dynamics, and 3) incorporate and correct the forward modeling physics in inverse estimation. We model both observation and unknown variables over geometric space and learn the inverse mapping in the latent space as a function of geometry. We then disentangle the latent dynamics to its emission to the data domain and supervise the model by physics laws governing the system. Furthermore, we incorporate the forward model in the form of a generative model in the inverse estimation to uncover and optimize the forward model parameters and deliver more accurate inverse solutions. Our methods demonstrate improved performance on patient-specific data and substantially increase its interpret-ability compared to existing deep learning methods.
Intended Audience:
Undergraduates, graduates, and experts. Those with interest in the topic.
Keep up with Inverse Problems Seminars on the Inverse Problems Seminar webpage.
To request an interpreter, please visit myaccess.rit.edu
Event Snapshot
When and Where
Who
This is an RIT Only Event
Interpreter Requested?
No