Keywords: Artifacts, Spinal CordUpon investigating a dataset of 804 studies for spinal fractures from two major vendors, the authors observed that 11% of the water-fat images in the studies are mis-labelled. This motivated the development of an automated algorithm to correct the mis-labelling. We used a 2D CNN based deep learning model to classify the images correctly with the aim of reducing error and fatigue in clinical diagnosis caused by such mis-labeling, as well as providing correct labels for further AI workflow. We also demonstrated the use of active learning in this problem by achieving the same test-error with fewer labels than using the entire training data.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords