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Abstract #2771

The Role of Optimizing Loss Functions in Transfer Learning to Address Data Scarcity

Simon Graf1,2, Walter Wohlgemuth1,2, and Andreas Deistung1,2
1Medical Physics Group, University Clinic and Outpatient Clinic for Radiology, University Hospital Halle (Saale), Halle (Saale), Germany, 2Halle MR Imaging Core Facility, Medical Faculty, Martin-Luther-University Halle-Wittenberg, Halle (Saale), Germany

Synopsis

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.

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