Events for February 24, 2021
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Medical Imaging Seminar
Wed, Feb 24, 2021 @ 09:00 AM - 10:00 AM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Yannick Bliesener, Electrical and Computer Engineering, University of Southern California
Talk Title: Measurement Uncertainty in DCE-MRI of Brain Tumors
Series: Medical Imaging Seminar Series
Abstract: Abnormal vasculature is a common symptom of many diseases of the brain such as Alzheimer's disease, multiple sclerosis, and brain cancer. Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCE-MRI) enables assessment of relevant neurovascular parameters by monitoring time-varying enhancement patterns in tissue after intra-venous contrast agent injection. The method has the potential to provide powerful biomarkers for brain tumors, including vascularity, blood brain barrier leakage, tumor progression vs regression, and probability of survival prediction. However, it is challenged by low precision. This talk will discuss my attempts to quantify and improve the accuracy and precision of high-resolution whole brain DCE-MRI of brain tumors.
Biography: Yannick Bliesener is a Ph.D. candidate in the Magnetic Resonance Engineering Laboratory at the University of Southern California. He obtained his B.Sc. and M.Sc. degree in Electrical Engineering at Hamburg University of Technology in Germany, before joining the Ming Hsieh Department of Electrical and Computer Engineering. His research focuses on the development and improvement of algorithms for DCE-MRI and real-time speech MRI. Specifically, he is concerned with the detection and alleviation of error sources to enhance reproducibility and repeatability of quantitative MRI.
Host: Krishna Nayak, knayak@usc.edu
Webcast: https://usc.zoom.us/j/94148553754Location: Online
WebCast Link: https://usc.zoom.us/j/94148553754
Audiences: Everyone Is Invited
Contact: Talyia White
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Center for Cyber-Physical Systems and Internet of Things and Ming Hsieh Institute Seminar
Wed, Feb 24, 2021 @ 02:00 PM - 03:00 PM
Ming Hsieh Department of Electrical and Computer Engineering
Conferences, Lectures, & Seminars
Speaker: Baihong Jin , Department of Electrical Engineering and Computer Sciences, University of California, Berkeley
Talk Title: Incipient Anomaly Detection with Machine Learning
Series: Center for Cyber-Physical Systems and Internet of Things
Abstract: Anomaly detection techniques are important in system health monitoring applications (e.g., fault detection and disease diagnosis). By recognizing suspicious patterns in data, anomaly detection models can tell whether a system has degraded from the normal operating condition into a faulty or diseased state. To avoid unnecessary losses, it is desirable to have a way to identify incipient anomalies, i.e. to detect potential problems in their early stages of development. In buildings, early detection of incipient faults can help reduce maintenance and repair costs, save energy, and enhance occupant comfort. In healthcare, if incipient diseases can be discovered early, effective treatments can be applied and can prevent diseases from progressing into more severe stages. We will show that ensemble learning methods can give improved performance on incipient anomalies and identify common pitfalls in these models through extensive experiments on two real-world applications-”detection of chiller faults and diagnosing diabetic retinopathy diseases. A theoretical analysis that compares the two popular strategies for extracting uncertainty information will also be given. We will also discuss how to design more effective ensemble models for detecting incipient anomalies.
Biography: Dr. Baihong Jin is currently a postdoctoral scholar at the Department of Electrical Engineering and Computer Sciences at University of California, Berkeley, where he received his PhD degree. Before that, he received a B.S. degree in microelectronics from Peking University, Beijing, China. Baihong is also a research affiliate in the Energy Technologies Area at the Lawrence Berkeley National Lab. Baihong's research interests include machine learning, fault management, and anomaly detection techniques, with a focus on their applications in energy cyber-physical systems and healthcare AI. Baihong is a recipient of the Lotfi A. Zadeh Prize for his dissertation research at UC Berkeley.
Host: Pierluigi Nuzzo, nuzzo@usc.edu
Webcast: https://usc.zoom.us/webinar/register/WN_Qk4-7AthThudso7LXs2OiALocation: Online
WebCast Link: https://usc.zoom.us/webinar/register/WN_Qk4-7AthThudso7LXs2OiA
Audiences: Everyone Is Invited
Contact: Talyia White
