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Then, a self-supervised feature orthogonalization loss is introduced to help learn modality heterogeneity features while maintaining maximal task-consistent category distinguishability ofmodalities. Finally, six encoded clinical information are utilized to avoid prediction bias and improve prediction ability further. Our three-fold cross-validation experiments demonstrate that our method surpasses clinicians and other multi-modal fusion methods with an accuracy of 80.06%, a true-positive rate of 81.81%, and a true-negati