RATNet applies analogical reasoning via a cyclic pre-training strategy to outperform prior foundation models in GI endoscopy diagnosis across diagnosis, few-shot, zero-shot, robustness, adaptation, and federated scenarios.
Medical image analysis91, 102996 (2024)
2 Pith papers cite this work. Polarity classification is still indexing.
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KEPIL integrates medical ontologies and a semantic contrastive loss into vision-language models to achieve state-of-the-art prompt-robust zero-shot disease detection in radiology, with reported AUC gains of 6.37% on CheXpert under prompt variations.
citing papers explorer
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Analogical Reasoning as a Doctor: A Foundation Model for Gastrointestinal Endoscopy Diagnosis
RATNet applies analogical reasoning via a cyclic pre-training strategy to outperform prior foundation models in GI endoscopy diagnosis across diagnosis, few-shot, zero-shot, robustness, adaptation, and federated scenarios.
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KEPIL: Knowledge-Enhanced Prompt-Image Learning for Prompt-Robust Disease Detection
KEPIL integrates medical ontologies and a semantic contrastive loss into vision-language models to achieve state-of-the-art prompt-robust zero-shot disease detection in radiology, with reported AUC gains of 6.37% on CheXpert under prompt variations.