Talk Title: Computer-Aided Diagnosis of Thoracic CT Scans Through Multiple Instance Transfer Learning
Talk Abstract: Computer-aided diagnosis systems have demonstrated significant potential in improving patient care and clinical outcomes by providing more extensive information to clinicians. The development of these systems typically requires a large amount of well-annotated data, which can be challenging to acquire in medical imaging. Several techniques have been investigated in an attempt to overcome insufficient data, including transfer learning, or the application of a pre-trained model to a new domain and/or task. The successful translation of transfer learning models to complex medical imaging problems holds significant potential and could lead to widespread clinical implementation.
However, transfer learning techniques often fail translate effectively because they are limited by the domain in which they were initially trained. For example, computed tomography (CT) is a powerful medical imaging modality that leverages 3D images in clinical decision-making, but transfer learning models are typically trained on 2D images and thus can not incorporate the additional information provided by the third dimension. This evaluation of the available data in a CT scan is inefficient and potentially does not effectively improve clinical decisions. In this project, the 3D information available in CT scans is combined incorporated with transfer learning through a multiple instance learning (MIL) scheme, which can individually assess 2D images and form a collective 3D prediction based on the 2D information, similar to how a radiologist would read a CT scan. This approach has been applied to evaluate both COVID-19 and emphysema in CT thoracic CT scans and demonstrated strong clinical potential.
Bio: Jordan Fuhrman is a student in the Graduate Program in Medical Physics at the University of Chicago. Since joining the program after his graduation from the University of Alabama in 2017, Jordan’s research has focused on the investigation of computer-aided diagnosis techniques for evaluating CT scans. Generally, this includes implementation of machine learning, deep learning, and computer vision algorithms to accomplish such tasks as disease detection, image segmentation, and prognosis assessments. His primary research interests lie in the development of novel approaches that incorporate the full wealth of information in CT scans to better inform clinical predictions, the exploration of explainable, interpretable outputs to improve clinical understanding of deep learning algorithm performance, and the early detection and prediction of patient progress to inform clinical decisions (e.g., most appropriate treatment) and improve patient outcomes. His work has largely focused on incidental disease assessment in low-dose CT lung screening scans, including emphysema, osteoporosis, and coronary artery calcifications, but has also included non-screening scan assessments of hypoxic ischemic brain injury and COVID-19. Jordan is a student member of both the American Association of Physicists in Medicine (AAPM) and the Society of Photo-optical Instrumentation Engineers (SPIE).