+DS vLE: Analysis of CT Scan Imaging Data with Machine Learning: Classification, Detection, and Segmentation of Abnormalities

Medical image analysis with machine learning holds immense promise for accelerating the radiology workflow and benefiting patient care. Computed tomography (CT) is a medical imaging technique that produces a high-resolution volumetric image of the internal organs. CT scans can be used to diagnose and monitor a wide range of conditions including cancer, fractures, and infections. However, interpreting a chest CT scan requires over 12 years of postsecondary education and painstaking manual inspection of hundreds of 2D slices. There is thus significant interest in developing machine learning models that can automatically interpret chest CT images. In this session, a variety of machine learning models for automated chest CT interpretation are introduced, including slice and volume-based convolutional neural networks and approaches for classifying, detecting, and segmenting abnormal findings.

Before the session, all registrants will receive an e-mail with a link and meeting information.

This session is part of the Duke+DataScience (+DS) program virtual learning experiences (vLEs). To learn more, please visit https://plus.datascience.duke.edu