Transfer learning from a sparsely annotated dataset of 3D medical images
Gabriel Efrain Humpire-Mamani Colin Jacobs Mathias Prokop Bram van Ginneken Nikolas Lessmann
Transfer learning leverages pre-trained model features from a large dataset
to save time and resources when training new models for various tasks,
potentially enhancing performance. Due to the lack of large datasets in the
medical imaging domain, transfer learning from one medical imaging model to
other medical imaging models has not been widely explored. This study explores
the use of transfer learning to improve the performance of deep convolutional
neural networks for organ segmentation in medical imaging. A base segmentation
model (3D U-Net) was trained on a large and sparsely annotated dataset; its
weights were used for transfer learning on four new down-stream segmentation
tasks for which a fully annotated dataset was available. We analyzed the
training set size's influence to simulate scarce data. The results showed that
transfer learning from the base model was beneficial when small datasets were
available, providing significant performance improvements; where fine-tuning
the base model is more beneficial than updating all the network weights with
vanilla transfer learning. Transfer learning with fine-tuning increased the
performance by up to 0.129 (+28\%) Dice score than experiments trained from
scratch, and on average 23 experiments increased the performance by 0.029 Dice
score in the new segmentation tasks. The study also showed that cross-modality
transfer learning using CT scans was beneficial. The findings of this study
demonstrate the potential of transfer learning to improve the efficiency of
annotation and increase the accessibility of accurate organ segmentation in
medical imaging, ultimately leading to improved patient care. We made the
network definition and weights publicly available to benefit other users and
researchers.