AMF-MedIT: An Efficient Align-Modulation-Fusion Framework for Medical Image-Tabular Data

Multimodal medical analysis combining image and tabular data has gained increasing attention. However, effective fusion remains challenging due to cross-modal discrepancies in feature dimensions and modality contributions, as well as the noise from high-dimensional tabular inputs. To address these problems, we present AMF-MedIT, an efficient Align-Modulation-Fusion framework for medical image and tabular data integration, particularly under data-scarce conditions. To harmonize dimension discrepancies and dynamically adjust modality contributions, we propose the Adaptive Modulation and Fusion (AMF) module, a novel modulation-based fusion paradigm with a streamlined architecture. We first derive the modulation objectives and introduce a modality confidence ratio, enabling the incorporation of prior knowledge into the fusion process. Then, the feature masks, density and leakage losses are proposed to achieve the modulation objectives. Additionally, we introduce FT-Mamba, a powerful tabular encoder leveraging a selective mechanism to handle noisy medical tabular data efficiently. Furthermore, interpretability studies are conducted to explore how different tabular encoders supervise the imaging modality during contrastive pretraining for the first time. Extensive experiments demonstrate that AMF-MedIT achieves a superior balance between multimodal performance and data efficiency while showing strong adaptability to incomplete tabular data. Interpretability analysis also highlights FT-Mamba's capabilities in extracting distinct tabular features and guiding the image encoder toward more accurate and flexible attention patterns.