HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling
<|reference_start|>HyperFusion: A Hypernetwork Approach to Multimodal Integration of Tabular and Medical Imaging Data for Predictive Modeling: The integration of diverse clinical modalities such as medical imaging and the tabular data obtained by the patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. The integrative analysis of multiple sources can provide a comprehensive understanding of a patient's condition and can enhance diagnoses and treatment decisions. Deep Neural Networks (DNNs) consistently showcase outstanding performance in a wide range of multimodal tasks in the medical domain. However, the complex endeavor of effectively merging medical imaging with clinical, demographic and genetic information represented as numerical tabular data remains a highly active and ongoing research pursuit.   We present a novel framework based on hypernetworks to fuse clinical imaging and tabular data by conditioning the image processing on the EHR's values and measurements. This approach aims to leverage the complementary information present in these modalities to enhance the accuracy of various medical applications. We demonstrate the strength and the generality of our method on two different brain Magnetic Resonance Imaging (MRI) analysis tasks, namely, brain age prediction conditioned by subject's sex, and multiclass Alzheimer's Disease (AD) classification conditioned by tabular data. We show that our framework outperforms both single-modality models and state-of-the-art MRI-tabular data fusion methods. The code, enclosed to this manuscript will be made publicly available.<|reference_end|>
@article{duenias2024hyperfusion:,
    title={HyperFusion: A Hypernetwork Approach to Multimodal Integration of
  Tabular and Medical Imaging Data for Predictive Modeling},
    author={Daniel Duenias, Brennan Nichyporuk, Tal Arbel, Tammy Riklin Raviv},
    journal={arXiv preprint arXiv:2403.13319},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.13319},
    primaryClass={cs.CV cs.LG eess.IV}
}