Revisiting Integration of Image and Metadata for DICOM Series Classification: Cross-Attention and Dictionary Learning
The framework jointly models image content and DICOM metadata using cross-modal attention to classify entire MRI series. It handles missing metadata directly without imputation by using a sparse encoder that learns feature dictionaries for observed attributes. This method achieves state-of-the-art performance on the Duke Liver MRI dataset by explicitly modeling the interaction between visual data and structured clinical information.