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Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging

Research Medical/Healthcare AI

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Merged summary

TL;DR - MseaCL is a multimodal contrastive learning framework for 3D pediatric brain MRI and radiology reports that reduces false negatives by using semantic similarity between reports as a guiding signal, yielding large downstream gains in clinical tasks.

  • Targets a known flaw in standard contrastive learning: treating all non-paired samples as negatives creates false negatives when samples share high-level semantic attributes, common in medical data.
  • Uses semantic similarity between radiology reports to guide alignment during pretraining, rather than assuming strict instance-based correspondence within a batch.
  • Trained on a pediatric cohort of 3D brain MRI scans paired with radiology reports.
  • Reports at least a 22.6% AUC increase on pediatric brain tumor molecular classification when used as a pretraining stage.

Sources (1)

Multimodal Semantic-Aware Contrastive Learning For False Negative Mitigation in 3D Medical Imaging

arXiv cs.LG Sara Ketabi, Matthias W. Wagner, Cynthia Hawkins, Uri Tabori, Birgit Betina Ertl-Wagner, Farzad Khalvati 2026-07-16 arXiv:2607.14995
Public signals Semantic Scholar citations 0 · Semantic Scholar influential citations 0
Providers: Hugging Face · N/A OpenAlex · N/A Publisher · N/A Semantic Scholar · Citations 0 · Influential citations 0 X · N/A Fetched 2026-08-10 02:58:06.797489 UTC

TL;DR - MseaCL is a multimodal contrastive learning framework for 3D pediatric brain MRI and radiology reports that reduces false negatives by using semantic similarity between reports as a guiding signal, yielding large downstream gains in clinical tasks.

  • Targets a known flaw in standard contrastive learning: treating all non-paired samples as negatives creates false negatives when samples share high-level semantic attributes, common in medical data.
  • Uses semantic similarity between radiology reports to guide alignment during pretraining, rather than assuming strict instance-based correspondence within a batch.
  • Trained on a pediatric cohort of 3D brain MRI scans paired with radiology reports.
  • Reports at least a 22.6% AUC increase on pediatric brain tumor molecular classification when used as a pretraining stage.
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