Peripheral edema is the most common symptom of heart failure. Reliable measurements of edema and continuous monitoring of trends provide critical clinical information and can be used for averting episodes of acute decompensation and hospitalizations. Students will do research on how to measure and track edema using videos of the skin during the edema pitting-test. They will design and experiment with deep learning models and video preprocessing techniques.
Lab: Kostic Lab
Direct Supervisor: Zoran Kostic
Position Dates: 5/1/2019 - 8/31/2019
Hours per Week: 20
Paid Position: Yes
Number of positions: 3
Qualifications: Machine learning, deep Learning.
Eligibility: Junior, Senior, Master's