Multi-View Synergistic Learning with Vision-Language Adaption for Low-Resource Biomedical Image Classification

ArXi:2604.23977v1 Announce Type: new Accurate biomedical image classification under low-resource conditions remains challenging due to limited annotations, subtle inter-class visual differences, and complex disease semantics. While vision--language models offer a promising foundation for mitigating data scarcity, their effective adaptation in biomedical settings is constrained by the need for parameter-efficient tuning alongside fine-grained and semantically consistent representation learning.