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Tables Guide Vision: Learning to See the Heart through Tabular Data

Marta Hasny,Maxime Di Folco,Keno Bressem,Julia A. Schnabel

Published 2025 in Unknown venue

ABSTRACT

Contrastive learning methods in computer vision typically rely on augmented views of the same image or multimodal pretraining strategies that align paired modalities. However, these approaches often overlook semantic relationships between distinct instances, leading to false negatives when semantically similar samples are treated as negatives. This limitation is especially critical in medical imaging domains such as cardiology, where demographic and clinical attributes play a critical role in assessing disease risk and patient outcomes. We introduce a tabular-guided contrastive learning framework that leverages clinically relevant tabular data to identify patient-level similarities and construct more meaningful pairs, enabling semantically aligned representation learning without requiring joint embeddings across modalities. Additionally, we adapt the k-NN algorithm for zero-shot prediction to overcome the lack of zero-shot capability in unimodal representations. We demonstrate the strength of our methods using a large cohort of short-axis cardiac MR images and clinical attributes, where tabular data helps to more effectively distinguish between patient subgroups. Evaluation on downstream tasks, including fine-tuning, linear probing, and zero-shot prediction of cardiovascular artery diseases and cardiac phenotypes, shows that incorporating tabular data guidance yields stronger visual representations than conventional methods that rely solely on image augmentation or combined image-tabular embeddings. Further, we show that our method can generalize to natural images by evaluating it on a car advertisement dataset. The code will be available on GitHub upon acceptance.

PUBLICATION RECORD

  • Publication year

    2025

  • Venue

    Unknown venue

  • Publication date

    2025-03-19

  • Fields of study

    Medicine, Computer Science

  • Identifiers
    arXiv 2503.14998
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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