Disentangled Representations Improve Morpho-Transcriptomic Integration
A recent preprint on arXiv (2608.14355) explores whether separating shared latent components from modality-specific ones can enhance multimodal representation learning for paired Hematoxylin & Eosin (H&E) and spatial transcriptomics (ST) datasets. This research assesses VAE-based and contrastive methods, both in their standard and disentangled forms, across two cancer cohorts under consistent experimental conditions. The evaluation of representations involves cross-modal reconstruction, downstream probing, and cross-modal probe transfer. Results indicate two significant trends: contrastive objectives often outperform in specific tasks, and the disentanglement of shared and unique components improves integration. These results hold relevance for computational pathology and multimodal learning in the field of biomedical research.
Key facts
- arXiv preprint 2608.14355
- Spatial transcriptomics enables simultaneous profiling of gene expression and tissue morphology
- Standard multimodal models often compress modalities without separating shared and modality-specific sources
- Study compares VAE-based and contrastive approaches in standard and disentangled variants
- Two cancer cohorts used under matched experimental conditions
- Evaluations include cross-modal reconstruction, downstream probing, and cross-modal probe transfer
- Contrastive objectives show performance advantages
- Disentanglement of shared and private components improves integration
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