A New Framework Based On Transfer Learning For Cross-Database Pneumonia Detection

Cross-database classification means that the model is able to apply to the serious disequilibrium of data distributions, and it is trained by one database while tested by another database. Thus, cross-database pneumonia detection is a challenging task. In this paper, we proposed a new framework based on transfer learning for cross-database pneumonia detection. First, based on transfer learning, we fine-tune a backbone that pre-trained on non-medical data by using a small amount of pneumonia images, which improves the detection performance on homogeneous dataset. Then in order to make the fine-tuned model applicable to cross-database classification, the adaptation layer combined with a self-learning strategy is proposed to retrain the model. The adaptation layer is to make the heterogeneous data distributions approximate and the self-learning strategy helps to tweak the model by generating pseudo-labels. Experiments on three pneumonia databases show that our proposed model completes the cross-database detection of pneumonia and shows good performance.