ddl-cxr

Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation

This is the checkpoints of DDL-CXR Paper.

Source code can be find in Code Repository.

Abstract

Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost and radiation dose. When clinical prediction is needed, the last available CXR image might have been outdated, leading to suboptimal predictions. To address this challenge, we propose DDL-CXR, a method that dynamically generates an up-to-date latent representation of the individualized CXR images. Our approach leverages latent diffusion models for patient-specific generation strategically conditioned on a previous CXR image and EHR time series, providing information regarding anatomical structures and disease progressions, respectively. In this way, the interaction across modalities could be better captured by the latent CXR generation process, ultimately improving the prediction performance.

Model Checkpoints

1. Autoencoder: DDL-CXR-VAE.
2. LDM: DDL-CXR-LDM.

BibTex

If you find our paper helpful, please cite:

@article{yao2024addressing,
  title={Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation},
  author={Yao, Wenfang and Liu, Chen and Yin, Kejing and Cheung, William K and Qin, Jing},
  journal={arXiv preprint arXiv:2410.17918},
  year={2024}
}