Dissertation Talk: Expert-Level Detection of Acute Intracranial Hemorrhage on Head Computed Tomography using Deep Learning

Presentation: Dissertation Talk: EE | May 10 | 1-2 p.m. |  Sutardja Dai Hall


 Electrical Engineering and Computer Sciences (EECS)

Computed tomography (CT) of the head is the workhorse medical imaging modality used worldwide to diagnose neurologic emergencies. However, these grayscale images are limited by low signal-to-noise, poor contrast, and a high incidence of image artifacts. A unique challenge is to identify, with perfect or near-perfect sensitivity and high specificity, often tiny subtle abnormalities occupying 106 pixels. We used a single-stage, end-to-end, fully convolutional neural network to produce state-of-the-art exam-level classification performance on an independent test set, comparable to that of U.S. board-certified radiologists, in addition to robust localization of abnormalities including some that are missed by radiologists, both of which are important to this application.

 jeannguyen@eecs.berkeley.edu, 510-642-9413