Deep Learning for Pneumothorax Detection and Localization in Chest Radiographs
This work addresses early detection of pneumothorax, a critical condition, for medical imaging applications, but it is incremental as it compares existing methods on a specific dataset.
The study tackled pneumothorax detection and localization in chest radiographs by evaluating three deep learning techniques, achieving AUCs of 0.96, 0.93, and 0.92, with an ensemble approach also reviewed.
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.