"Multimodal Generative Models Bridge Whole-Slide and Mass Spectrometry Imaging for Biomarker Discovery"

"his study proposes an innovative method for multimodal data fusion and potential biomarker mining. By employing deep learning, the study combines the gigapixel-level morphological information from Whole Slide Images (WSI) with the chemical spatial distribution data from Mass Spectrometry Imaging (MSI). By integrating the Pix2Pix model with WSI pyramid structural information, the fusion model undergoes adversarial training, achieving both cross-scale and cross-modal fusion. This approach integrates four networks and six models into an automated, unsupervised pipeline for biomarker discovery. It simplifies the complex task of multimodal medical data analysis into a regression problem and can be applied to other medical datasets and disease studies. The model identifies both known and potential biomarkers, assigning each a ranking score, thereby providing a reliable automated method for biomarker discovery."