Keywords: Other AI/ML, Data Analysis, Predictive deep learning, MLOps
Motivation: Prospective quality assessment of synthetic CT images by predicting an accuracy metric. Such a score can be an indication of confidence of model prediction or be used as a feedback for performance of the model.
Goal(s): Prediction of mean absolute value of synthetic CT image without a reference CT image
Approach: A deep learning framework which is trained to predict MAE metric of a given image.
Results: The proposed QMetNet model learns to predict the MAE metric on unseen data in a reliable manner without a reference image.
Impact: This novel framework makes it possible to train models to predict a choice of metrics as suitable for different applications. It could be a potential solution to provide confidence of prediction of a model to ease adoption of AI solutions.
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