We propose an algorithm to simultaneously prune and train CNNs, leading to networks which have increased generalisability across imaging sites. Through segmentation of data from the ABIDE dataset, we show that through reducing the number of parameters in the network throughout training, we are able to reduce model overfitting, creating a model which is more robust to expect image variations across scanners. We also introduce a novel Targeted Dropout algorithm, which aids the process of model pruning. We demonstrate the approach on a UNet architecture, the basis of nearly all segmentation approaches across medical imaging.
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