Abstract
Spatial transcriptomics (ST) enables the simultaneous profiling
of gene expression and tissue morphology, creating an opportunity to
learn multimodal representations capturing shared morpho-transcriptomic
structure. However, standard multimodal models often compress modalities
into a common latent space without explicitly separating shared
and modality-specific sources of variation, which may limit downstream
utility. We investigate whether explicit disentanglement of shared and
private latent components improves multimodal representation learning
for paired Hematoxylin & Eosin (H&E) and ST data. We compare VAEbased
and contrastive approaches, each in standard and disentangled
variants, across two cancer cohorts under matched experimental conditions.
Representations are evaluated using cross-modal reconstruction,
downstream probing and cross-modal probe transfer. The experiments
suggest two main trends. First, contrastive objectives yield higher downstream
probing performance than VAE-based models. Second, disentangled
variants improve the selected reconstruction and probing metrics,
although the gains depend on the model family, task, direction, and disentanglement
strength. Overall, our results suggest that explicitly factorizing
shared and modality-specific information can improve multimodal
representation learning for spatial transcriptomics and provides a useful
evaluation framework for future foundation models.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/COMPAYL_036.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: https://papers.miccai.org/miccai-2026-sat/supp/COMPAYL_036_supp.pdf
Link to Open Review
BibTex
@InProceedings{OstJul_Disentangled_MICCAISAT2026,
author = { Ostermaier, Julian AND Ruyter, Swann AND Dorent, Reuben AND Racoceanu, Daniel},
title = { { Disentangled Shared Representations Improve Morpho-Transcriptomic Integration } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17251},
month = {pending},
page = {pending}
}