Weakly Supervised Segmentation Framework with Uncertainty: A Study on Pneumothorax Segmentation in Chest X-ray

Abstract

Pneumothorax is a critical abnormality that shall be treated with higher priority, and hence a computerized triage scheme is needed. A deep-learning-based framework to automatically segment the pneumothorax in chest X-rays is developed to support the realization of a triage system. Since a large number of pixel-level annotations is commonly needed but difficult to obtain for deep learning model, we propose a weakly supervised framework that allows partial training data to be weakly annotated with only image-level labels. We employ the attention masks derived from an image-level classification model as the pixel-level masks for those weakly-annotated data. Because the attention masks are rough and may have errors, we further develop a spatial label smoothing regularization technique to explore the uncertainty for the incorrectness of the attention masks in the training of segmentation model. Experimental results show that the proposed weakly supervised segmentation algorithm relieves the need of well-annotated data and yield satisfactory performance on the pneumothorax segmentation.