His-MMDM: Multi-domain and Multi-omics Translation of Histopathology Images with Diffusion Models

Generative AI (GenAI) has advanced computational pathology through various image translation models. These models synthesize histopathological images from existing ones, facilitating tasks such as color normalization and virtual staining. Current models, while effective, are mostly dedicated to specific source-target domain pairs and lack scalability for multi-domain translations. Here we introduce His-MMDM, a diffusion model-based framework enabling multi-domain and multi-omics histopathological image translation. His-MMDM can translate images across an unlimited number of categorical domains, enabling new applications like the translation of tumor images across various tumor types, while performing comparably to dedicated models on previous tasks such as transforming cryosectioned images to formalin-fixed paraffin-embedded (FFPE) ones. Additionally, it can perform genomics- and/or transcriptomics-guided editing of histopathological images, illustrating the impact of driver mutations and oncogenic pathway alterations on tissue histopathology. These versatile capabilities position His-MMDM as a versatile tool in the GenAI toolkit for future pathologists. ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This publication is based upon work supported by the King Abdullah University of Science and Technology (KAUST) Office of Research Administration (ORA) under Award No REI/1/5234-01-01, REI/1/5414-01-01, RGC/3/4816-01-01, REI/1/5289-01-01, REI/1/5404-01-01, REI/1/5992-01-01, and URF/1/4663-01-01. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: The protocols for the human studies comply with all relevant ethical regulations and are approved by the Ethics Committee of The First Harbin Medical University Hospital and The Harbin Medical University Cancer Hospital (KY2021-42). The consent forms of the patients were waived before this research was carried out under the retrospective research protocol of the institutions. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The code and trained checkpoints of His-MMDM are available at https://github.com/lzx325/His-MMDM. Histopathology Data from TCGA used in this study are available from the Genomic Data Commons Portal of the National Cancer Institute ( https://gdc.cancer.gov/ ). Example data from The First Harbin Medical University Hospital and The Harbin Medical University Cancer Hospital are deposited at Zenodo (10.5281/zenodo.12636449) and are available from the lead contact upon reasonable request.