On the Effective Transfer Learning Strategy for Medical Image Analysis in Deep Learning

摘要

In this study, we focus on exploring different strategies of transfer learning for medical applications. Firstly, we report competitive results indicating that convolutional neural networks (CNNs) that were pre-trained with different annotations could have diverse effects on the performance of medical image analysis, especially for segmentation tasks. Then, we present our further explorations of transferring different components of the CNNs, which revealed the importance of the decoder on medical segmentation. Finally, we demonstrate the advantages and disadvantages of transfer learning methods based on model integration. These observations present novel aspects of transfer learning for visual tasks in the medical field, and we expect that these discoveries will encourage the exploration of more effective transfer learning strategies for CNN-based medical image analysis.