{"id":2669,"date":"2020-06-09T00:49:28","date_gmt":"2020-06-09T05:49:28","guid":{"rendered":"https:\/\/datascience.uchicago.edu\/?post_type=pt_people&p=2669"},"modified":"2021-05-05T17:01:51","modified_gmt":"2021-05-05T22:01:51","slug":"bradie-ferguson","status":"publish","type":"pt_people","link":"https:\/\/datascience.uchicago.edu\/people\/bradie-ferguson\/","title":{"rendered":"Bradie Ferguson"},"content":{"rendered":"
Project:\u00a0<\/strong>Radiomic Texture Analysis of Immunofluorescence Images of Lupus Nephritis Biopsies to Predict Patient Progression to End Stage Renal Disease<\/span><\/p>\n Mentor:<\/strong> Maryellen Giger<\/a>, Department of Radiology<\/p>\n Research Area Keywords:<\/strong> Machine Learning & AI \/\/ Image Analysis \/\/ Medicine & Health<\/p>\n Project Description:\u00a0<\/b>Bradie Ferguson is a pre-med senior at the University of Washington studying bioengineering and chemistry. This summer, she continued work with Drs. Maryellen Giger<\/a> and Madeleine Durkee<\/a> on an image analysis project on microscopic images of lupus nephritis biopsies. The goal was to create a multi-feature classifier that can distinguish between patients that progressed to end stage renal disease (ESRD+) and those that did not progress (ESRD-). To accomplish this, radiomic texture analysis was utilized with future plans of using machine learning.<\/span><\/p>\n