  Since the strong comorbid similarity in NDDs, such as attention-deficit
hyperactivity disorder, can interfere with the accurate diagnosis of autism
spectrum disorder (ASD), identifying unknown classes is extremely crucial and
challenging from NDDs. We design a novel open set recognition framework for
ASD-aided diagnosis (OpenNDD), which trains a model by combining autoencoder
and adversarial reciprocal points learning to distinguish in-distribution and
out-of-distribution categories as well as identify ASD accurately. Considering
the strong similarities between NDDs, we present a joint scaling method by
Min-Max scaling combined with Standardization (MMS) to increase the differences
between classes for better distinguishing unknown NDDs. We conduct the
experiments in 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
achieves promising performance, where the accuracy is 77.38%, AUROC is 75.53%
and the open set classification rate is as high as 59.43%.
