Inception-GAN for Semi-supervised Detection of Pneumonia in Chest X-rays

Recent advances in Deep Learning have led to the development of supervised models to detect anomalies in medical images such as pneumonia in chest X-rays. Automatic detection of such anomalies can help clinicians with faster decision making and treatment planning for patients. Nonetheless, supervised models require complete labeled training data with all possible labels (i.e., positive and negative), which are cumbersome and expensive to obtain. We propose an adversarial learning-based...