🎤 Talk at Glasgow AI4BioMed Lab!

💡 Talk Title

“CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding”

🖇️ Abstract

Radiology multimodal large language models (MLLMs) often generate clinically unsupported descriptions, posing serious risks in medical applications. We introduce Clinical Contrastive Decoding (CCD), a training-free inference framework that integrates structured clinical signals from radiology expert models to mitigate hallucinations. CCD refines token-level logits during generation through a dual-stage contrastive mechanism, enhancing clinical fidelity without modifying the base MLLM. On the MIMIC-CXR dataset,CCD yields up to 17% improvement in RadGraph-F1, providing a lightweight solution for bridging expert models and MLLMs in radiology.

🔗 Project Website: https://x-izhang.github.io/CCD/

📺 Slides

📍 Glasgow AI4BioMed Lab — An interdisciplinary research lab focusing on AI applications in biomedical sciences, based in Glasgow.

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When: Wednesday, January 28, 2026 at 2pm
Where: F121

See you there!