Laboratory Test-Guided Medical Image Generation for Multi-Modal Disease Prediction

The integration of laboratory tests and medical images is crucial in making accurate disease prediction. However, imaging data exhibits temporal sparsity, compared to frequently collected laboratory tests. This temporal sparsity limits effective multi-modal interaction, which in turn degrades the prediction accuracy. We address this issue by generating additional medical images at more time points, conditioned on the laboratory tests. Inspired by the pivotal role of organs in mediating laboratory tests and imaging abnormalities, we propose an Organ-Centric Modal-Shared Image Generator. It converts laboratory tests into imaging abnormalities through two key components: 1) Organ-Centric Graph: It positions organs as central nodes connecting laboratory tests and imaging abnormalities; and 2) Knowledge-Guided Modal-Shared Trajectory Module: It binds multi-modal features across time into a unified organ state trajectory. Experimental results demonstrate that our method improves multi-modal prediction performance across various diseases. Code is available at https://github.com/LyapunovStability/Lab_Guide_Med_Image _Gen.