This paper addresses these problems and tries to make models for pneumonia classification independent from those confounders. To be more precise, the authors propose an approach based on adversarial neural networks to address the dataset shift. This way they are able to train models that are more reliable and that can be used across hospitals. In general, what authors have found is that model confounding can be potential model confusion can be identified by evaluating how well confounders can be predicted from a model’s output. Apart from that, by using an adversarial training process they are able to make classification independent from the radiograph’s view and obtain better generalization performance with the test data form the new hospital. The starting point was extending each sample with an indicator of the view. So, each sample is essentially a tuple containing: image, label and indicator of view. Then they used transfer learning with DenseNet-121 with SGD optimizer. As we mentioned,is used. During this training process, two connected two neural networks train on the same data. The first network is the classifier, f. This network is trained to predict a pneumonia label y from a radiograph x. The second network is an adversary, d. This network is trained to predict the view v from the output score s of the classifier f. This is a very interesting use of this training approach. The optimization procedure consists of alternating between training the adversary network until it is optimal, then training the classifier to fool the adversary while still predicting pneumonia well.