Analysis of pathology in patients from heterogeneous datasets using machine learning techniques provide valuable information for identifying patients with carotid artery atherosclerosis disease. We propose and evaluate a method to automatically identify these patients based only on MR brain imaging findings in a dataset also containing multiple sclerosis patients and healthy control subjects. The features extracted using convolutional networks were discriminative, showing high accuracy rates (>96%) to distinguish between the three classes: atherosclerosis patients, multiple sclerosis patients or healthy controls. The method may help specialists in the diagnosis (specially in critical cases), and evaluation of disease activity.
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