Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence
Motivation: Data scarcity in medical imaging presents substantial challenges for training complex deep learning models aimed at solving inverse problems like field-to-susceptibility inversion.
Goal(s): This study investigates the effectiveness of pre-training and transfer learning, focusing specifically on loss function optimization during transfer learning.
Approach: An Adaptive 3D U-Net was pre-trained on synthetic data and subsequently fine-tuned on in-vivo brain datasets using various loss functions. The models were evaluated on 32 brain datasets.
Results: Using complex loss functions penalizing intensity and structural deviations during transfer learning resulted in improved susceptibility map accuracy, whereas their application during pre-training did not yield better outcomes.
Impact: This study demonstrated the potential of optimizing transfer learning to adapt pre-trained models, even from different training settings, to new target-specific data, highlighting the great potential of cross-domain knowledge transfer and fine-tuning in addressing data scarcity.
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