Transfer Learning with a Layer Dependent Regularization for Medical Image Segmentation

Sagie, N.; Greenspan, H.; Goldberger, J.

Transfer learning is a machine learning technique where a model trained on one task is used to initialize the learning procedure of a second related task which has only a small amount of training data. Transfer learning can also be used as a regularization procedure by penalizing the learned parameters if they deviate too much from their initial values. In this study we show that the learned parameters move apart from the source task as the image processing progresses along the network layers. To cope with this behaviour we propose a transfer regularization method based on monotonically decreasing regularization coefficients. We demonstrate the power of the proposed regularized transfer learning scheme on COVID-19 opacity task. Specifically, we show that it can improve the segmentation of coronavirus lesions in chest CT scans. © 2021, Springer Nature Switzerland AG.