MDL-CA: a multimodal deep learning approach with a cross attention mechanism for accurate brain cancer diagnosis

The integration of genomic and MRI data through the proposed cross-attention mechanism enables deeper biological understanding and improved diagnostic precision compared to single-modality and conventional fusion approaches. By effectively modeling interactions between molecular and anatomical features, MDL-CA advances the development of biologically informed, multimodal diagnostic systems for brain cancer. The results highlight the framework's potential to support early diagnosis and personalized treatment planning in clinical practice.