Orbital Lymphoproliferative Disorder Diagnosis with Incomplete Multimodal Images based on Self-/Cross-Representation and Hypergraph Ensemble

Orbital lymphoproliferative disorders (OLPDs) are complex orbital mass-like lesions ranging from benign to malignant. Precise preoperative diagnosis of OLPDs holds profound importance in facilitating timely and effective patient management. Recent studies have shown that exploiting multimodal images can boost the performance in identifying different orbital lesions. However, one or several imaging modalities are sometimes missing in practical applications, which has not yet been properly addressed in existing studies. To this end, we propose a novel OLPD diagnostic method with incomplete multimodal images based on self-/cross-representation and hypergraph ensemble. Specifically, in the first stage, we develop a self-representation network to extract unimodal features and a cross-representation network to impute missing features. In the second stage, by using unimodal features as input, we construct a hypergraph for each modality to make unimodal diagnosis; while for multimodal diagnosis we conduct a multi-view grouping fusion method to reduce the semantic gap between multimodal features and fuse multiple unimodal hypergraphs as multimodal hypergraph to perform multimodal diagnosis. In the third stage, we propose an ensemble strategy that incorporates unimodal diagnosis and multimodal diagnosis to accomplish the final decision. Extensive experiments demonstrate that the proposed model outperforms the state-of-the-art approaches.