Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.
Medical Image Analysis p
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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.
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The Learnability Gap in Medical Latent Diffusion
Pretrained autoencoders in medical latent diffusion encode discriminative features well for reconstruction but structure their latent spaces in ways that hinder classifier learning, a gap that persists across architectures and is not closed by domain fine-tuning.
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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.