MedAI #33: Deep Learning Methods for Electrocardiograms and Echocardiograms | Weston Hughes

Title: Deep Learning Methods for Electrocardiograms and Echocardiograms Speaker: Weston Hughes Abstract: In this talk, we will discuss two recently published deep learning methods we’ve developed at Stanford and UCSF for the understanding of ECG and echocardiogram data. First, we’ll discuss the development and evaluation of a convolutional neural network for multi class ECG interpretation which outperforms cardiologists and currently used ECG algorithms. Second, we’ll discuss a computer vision system for evaluating a range of biomarkers from echocardiogram videos. In our discussion of both papers, we’ll emphasize different analyses aiming to explain and interpret the models in different ways. Speaker Bio: Weston Hughes is a 3rd year PhD student in the Computer Science department at Stanford, co-advised by James Zou in Biomedical Data Science and Euan Ashley in Cardiology. His research focuses on applying deep learning and computer vision techniques to cardiovascular imaging data, including electrocardiogra