DBMF: A Dual-Branch Multimodal Framework for Out-of-Distribution Detection

arXiv:2604.08261v2 Announce Type: replace-cross Abstract: The complex and dynamic real-world clinical environment demands reliable deep learning (DL) systems. Out-of-distribution (OOD) detection plays a critical role in enhancing the reliability and generalizability of DL models when encountering data that deviate from the training distribution, such as unseen disease cases. However, existing OOD detection methods typically rely either on a single visual modality or solely on image-text matching