A patch-based and multi-instance learning strategy for pneumothorax classification on chest X-rays

Abstract

Pneumothorax is a lung emergency. Automated computer-aid pneumothorax diagnosis based on chest X-ray can help reduce the diagnostic time and save valuable time for the treatment. A total of 21,759 patient’s frontal-view chest X-ray images from one medical center are used in this study. The dataset is divided into two categories: pneumothorax and non-pneumothorax, which are evaluated by two radiologists with over ten years of practical experience. A two-stage training for pneumothorax classification based on multi-instance learning (MIL) are proposed, first training a patch-level classifier, followed by an image-level classifier training, which is initialized with the patch pre-trained weights. The image-level classifier initialized with patch pre-trained weights achieves good classification performance with the F1-score, accuracy and recall of 0.869, 0.915 and 0.843 respectively, which are larger compared to that of the model initialized without patch pre-trained weights (0.785, 0.878 and 0.783). The two-stage training strategy can improve the performance of pneumothorax classification and does not require too high GPU memory and long training time.