Selecting Reliable Instances from ImageNet for Medical Image Domain Adaptation

Pre-training deep learning models on ImageNet and transferring the models to medical image applications facilitate to improve the medical image analysis and reduce the need for labeled medical image data. However, some images from ImageNet may be fundamentally different from medical images in feature representation and lead to the negative transfer effects. To deal with this issue, we propose a novel strategy based on evidence theory to select reliable instances from ImageNet for medical image domain adaptation. Specifically, we formulate an evidential mass function to measure the ignorance and reliability of the images from ImageNet with respect to the classification tasks of medical images. Through selecting reliable instances with low ignorance degree from ImageNet, we can enhance the transfer performances of deep neural networks in medical image domain adaptation. Moreover, the proposed data selection strategy is independent of specific learning algorithm and can be viewed as a common preprocessing technique. Numerical experiments on tomography images, X-Ray images, and ultrasound images are given to comprehensively demonstrate the effectiveness of the selection strategy.