OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection

Neurodevelopmental disorders (NDDs) are a highly prevalent group of disorders and represent strong clinical behavioral similarities, and that make it very challenging for accurate identification of different NDDs such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). Moreover, there is no reliable physiological markers for NDDs diagnosis and it solely relies on psychological evaluation criteria. However, it is crucial to prevent misdiagnosis and underdiagnosis by intelligent assisted diagnosis, which is closely related to the follow-up corresponding treatment. In order to relieve these issues, we propose a novel open set recognition framework for NDDs screening and detection, which is the first application of open set recognition in this field. It combines auto encoder and adversarial reciprocal points open set recognition to accurately identify known classes as well as recognize classes never encountered. And considering the strong similarities between different subjects, we present a joint scaling method called MMS to distinguish unknown disorders. To validate the feasibility of our presented method, we design a reciprocal opposition experiment protocol on the hybrid datasets from Autism Brain Imaging Data Exchange I (ABIDE I) and THE ADHD-200 SAMPLE (ADHD-200) with 791 samples from four sites and the results demonstrate the superiority on various metrics. Our OpenNDD has achieved promising performance, where the accuracy is 77.38%, AUROC is 75.53% and the open set classification rate is as high as 59.43%.