Deep multi-instance transfer learning for pneumothorax classification in Chest X-Ray images.

PURPOSE Pneumothorax is a life-threatening emergency that requires immediate treatment. Frontal-view chest X-ray images are typically used for pneumothorax detection in clinical practice. However, manual review of radiographs is time-consuming, labor-intensive, and highly dependent on the experience of radiologists, which may lead to misdiagnosis. Here, we aim to develop a reliable automatic classification method to assist radiologists in rapidly and accurately diagnosing pneumothorax in frontal chest radiographs. METHODS A novel residual neural network (ResNet)-based two-stage deep-learning strategy is proposed for pneumothorax identification: local feature learning (LFL) followed by global multi-instance learning (GMIL). Most of the nonlesion regions in the images are removed for learning discriminative features. Two datasets are used for large-scale validation: a private dataset (27,955 frontal-view chest X-ray images) and a public dataset (the National Institutes of Health (NIH) ChestX-ray14; 112,120 frontal-view X-ray images). The model performance of the identification was evaluated using the accuracy, precision, recall, specificity, F1-score, receiver operating characteristic (ROC), and area under ROC curve (AUC). 5-fold cross validation is conducted on the datasets, and then the mean and standard deviation of the above-mentioned metrics are calculated to assess the overall performance of the model. RESULTS The experimental results demonstrate that the proposed learning strategy can achieve state-of-the-art performance on the NIH dataset with an accuracy, AUC, precision, recall, specificity, and F1-score of 94.4 ± 0.7%, 97.3 ± 0.5%, 94.2 ± 0.3%, 94.6 ± 1.5%, 94.2 ± 0.4%, and 94.4 ± 0.7%, respectively. CONCLUSIONS The experimental results demonstrate that our proposed CAD system is an efficient assistive tool in the identification of pneumothorax. This article is protected by copyright. All rights reserved.