ORC-GNN: A novel open set recognition based on graph neural network for multi-class classification of psychiatric disorders

Abstract

Open-set recognition (OSR) refers to the challenge of introducing classes not seen during model training into the test set. This issue is particularly critical in the medical field due to incomplete data collection and the continuous emergence of new and rare diseases. Medical OSR techniques necessitate not only the accurate classification of known cases but also the ability to detect unknown cases and send the corresponding information to experts for further diagnosis. However, there is a significant research gap in the current medical OSR field, which not only lacks research methods for OSR in psychiatric disorders, but also lacks detailed procedures for OSR evaluation based on neuroimaging. To address the challenges associated with the OSR of psychiatric disorders, we propose a method named the open-set risk collaborative consistency graph neural network (ORC-GNN). First, functional connectivity (FC) is used to extract measurable representations in the deep feature space by coordinating hemispheric and whole-brain networks, thereby achieving multi-level brain network feature fusion and regional communication. Subsequently, these representations are used to guide the model to adaptively learn the decision boundaries for known classes using the instance-level density awareness and to identify samples outside these boundaries as unknown. We introduce a novel open-risk margin loss (ORML) to balance empirical risk and open-space risk; this approach makes open-space risk quantifiable through the introduction of open-risk term. We evaluate our method using an integrated multi-class dataset and a tailored experimental protocol suited for psychiatric disorder-related OSR challenges. Compared to state-of-the-art techniques, ORC-GNN demonstrates significant performance improvements and yields important clinically interpretative information regarding the shared and distinct characteristics of multiple psychiatric disorders.