Keywords: CEST & MT, Machine Learning/Artificial IntelligenceDeep learning approaches have been widely applied to the MRI field. Among them, transformers have received increasing popularity due to their capability in handling multi modalities. Yet, transformers are hungry for large-scale data, which is expensive to collect. Here, we develop a novel memorizing transformer for small-scale multi-parametric MRI analysis. Empirically, we evaluate the proposed method on a dataset curated from 147 brain post-treatment malignant glioma cases for classifying treatment effect and tumor recurrence. The proposed memorizing transformer boosted a 5.15% improvement in the test area under the receiver-operating-characteristic curve (AUC) to the baseline transformer approaches.
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