Selective Cross-modal Feature Enhancement Through Collaborative Learning
Selective Cross-Modal Learning is introduced, a lightweight pre-fusion refinement framework that enriches unimodal embeddings by selectively incorporating complementary cues from concurrently available modalities and maintains its advantage under missing-pathology and missing-genomics conditions.