A Weakly supervised approach for thoracic diseases detection

Abstract

Medical image analysis has become increasingly important as it can effectively assist health care providers with precise clinical diagnosis decisions. Thoracic diseases are one of the leading causes of death in the U.S. Accurate and reliable thoracic disease classification plays a crucial role in early diagnosis and consequently proper treatment plans by providing great assistance to physicians. In the health care domain, clinical diagnoses and decisions are made based on specialist's opinions which is a tedious and error-prone process. Therefore, computer-aided systems are significantly applied to help health care administrations in decision making as a decision support. Deep learning approaches have shown promising potential in clinical image processing. In this study, an online feature selection method for efficient sampling of high-resolution X-ray images is proposed. A weakly-supervised approach that combines DenseNet-121 layers with a saliency sampling approach to extract the most informative parts or pixels of the input images is proposed. Unlike uniform sampling, the proposed approach preserves the spatial information of input images contributing to an accurate prediction. Performance of the proposed model is evaluated using two public chest X-ray datasets including ChestX-ray14 and CheXpert. The final model that aggregates the predictions over multiple sampling of the input image achieves the averaged AUC of 0.890 and 0.921 for ChestX-ray14 and CheXpert datasets, respectively. Our results for thoracic disease detection demonstrate that the proposed approach can effectively improve the performance of the thoracic disease prediction by identifying and preserving the most important parts of the input images.