Masked Omics Modeling for Multimodal Representation Learning across Histopathology and Molecular Profiles

• URL: http://arxiv.org/abs/2508.00969v1
• Date: Fri, 01 Aug 2025 15:29:26 GMT
• Title: Masked Omics Modeling for Multimodal Representation Learning across Histopathology and Molecular Profiles
• Authors: Lucas Robinet, Ahmad Berjaoui, Elizabeth Cohen-Jonathan Moyal,
• Abstract summary: Self-supervised learning has driven major advances in computational pathology.<n>However, histopathology alone often falls short for molecular characterization and understanding clinical outcomes.<n>We introduce MORPHEUS, a unified transformer-based pre-training framework that encodes both histopathology and multi-omics data into a shared latent space.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Self-supervised learning has driven major advances in computational pathology by enabling models to learn rich representations from hematoxylin and eosin (H&E)-stained cancer tissue. However, histopathology alone often falls short for molecular characterization and understanding clinical outcomes, as important information is contained in high-dimensional omics profiles like transcriptomics, methylomics, or genomics. In this work, we introduce MORPHEUS, a unified transformer-based pre-training framework that encodes both histopathology and multi-omics data into a shared latent space. At its core, MORPHEUS relies on a masked modeling objective applied to randomly selected omics portions, encouraging the model to learn biologically meaningful cross-modal relationships. The same pre-trained network can be applied to histopathology alone or in combination with any subset of omics modalities, seamlessly adapting to the available inputs. Additionally, MORPHEUS enables any-to-any omics generation, enabling one or more omics profiles to be inferred from any subset of modalities, including H&E alone. Pre-trained on a large pan-cancer cohort, MORPHEUS consistently outperforms state-of-the-art methods across diverse modality combinations and tasks, positioning itself as a promising framework for developing multimodal foundation models in oncology. The code is available at: https://github.com/Lucas-rbnt/MORPHEUS

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