Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images

• URL: http://arxiv.org/abs/2507.14670v1
• Date: Sat, 19 Jul 2025 15:45:12 GMT
• Title: Gene-DML: Dual-Pathway Multi-Level Discrimination for Gene Expression Prediction from Histopathology Images
• Authors: Yaxuan Song, Jianan Fan, Hang Chang, Weidong Cai,
• Abstract summary: Accurately predicting gene expression from histopathology images offers a scalable and non-invasive approach to molecular profiling.<n>Existing methods often underutilize the cross-modal representation alignment between histopathology images and gene expression profiles.<n>We propose Gene-DML, a unified framework that structures latent space through Dual-pathway Multi-Level discrimination.
• Score: 5.638556074980827
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Accurately predicting gene expression from histopathology images offers a scalable and non-invasive approach to molecular profiling, with significant implications for precision medicine and computational pathology. However, existing methods often underutilize the cross-modal representation alignment between histopathology images and gene expression profiles across multiple representational levels, thereby limiting their prediction performance. To address this, we propose Gene-DML, a unified framework that structures latent space through Dual-pathway Multi-Level discrimination to enhance correspondence between morphological and transcriptional modalities. The multi-scale instance-level discrimination pathway aligns hierarchical histopathology representations extracted at local, neighbor, and global levels with gene expression profiles, capturing scale-aware morphological-transcriptional relationships. In parallel, the cross-level instance-group discrimination pathway enforces structural consistency between individual (image/gene) instances and modality-crossed (gene/image, respectively) groups, strengthening the alignment across modalities. By jointly modelling fine-grained and structural-level discrimination, Gene-DML is able to learn robust cross-modal representations, enhancing both predictive accuracy and generalization across diverse biological contexts. Extensive experiments on public spatial transcriptomics datasets demonstrate that Gene-DML achieves state-of-the-art performance in gene expression prediction. The code and checkpoints will be released soon.

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