Keywords: Machine Learning/Artificial Intelligence, ModellingPrior characterization of treatment-effect and tumor recurrence using deep learning approaches have not optimized for spatial classification within a single lesion, which could improve surgical planning and treatment. 10mm patches of pre-surgical anatomical and physiological images surrounding the locations of histopathologically-confirmed tissue samples were used to train our models. Including physiological images, pretraining on unlabeled data in an autoencoding task, and training with an alternative cross-validation approach that enabled many networks to be ensembled, we achieved an ensembled test AUROC of 0.814 and generated spatial maps of tumor probability and model uncertainty. Performance decreased when removing any of these components.
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