  Pneumothorax is a critical condition that requires timely communication and
immediate action. In order to prevent significant morbidity or patient death,
early detection is crucial. For the task of pneumothorax detection, we study
the characteristics of three different deep learning techniques: (i)
convolutional neural networks, (ii) multiple-instance learning, and (iii) fully
convolutional networks. We perform a five-fold cross-validation on a dataset
consisting of 1003 chest X-ray images. ROC analysis yields AUCs of 0.96, 0.93,
and 0.92 for the three methods, respectively. We review the classification and
localization performance of these approaches as well as an ensemble of the
three aforementioned techniques.

Image and Video Processing;Computer Vision and Pattern Recognition;Machine Learning