OpenNDD: Open Set Recognition for Neurodevelopmental Disorders Detection
This work addresses the challenge of distinguishing ASD from unknown NDD classes in medical diagnosis, representing an incremental improvement with specific gains in open set recognition.
The paper tackled the problem of accurately diagnosing autism spectrum disorder (ASD) amidst similar neurodevelopmental disorders (NDDs) by developing an open set recognition framework called OpenNDD, which achieved an accuracy of 77.38%, AUROC of 75.53%, and an open set classification rate of 59.43%.
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%.