🚨 Preprint out — Clinical Contrastive Decoding！

Sat, 27 Sep 2025 00:00:00 +0000

“CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical Contrastive Decoding”. The preprint is now available.

For detailed model information and source code, please visit our Project Page: CCD

Overview

Multimodal large language models (MLLMs) have advanced radiology tasks by combining image and text understanding, but can sometimes produce inaccurate or unsupported clinical statements—so-called medical hallucinations. We introduce Clinical Contrastive Decoding (CCD), an inference-time method that leverages structured clinical signals (for example, symptom-level probabilities from specialist classifiers) to refine token-level logits during generation. CCD is designed to be applied without modifying base model weights or requiring external retrieval. In our evaluations on datasets such as MIMIC-CXR and IU-Xray, CCD yields consistent improvements in clinical metrics (for example, up to +17% RadGraph-F1 on MIMIC-CXR), reducing unsupported mentions while preserving overall fluency.

Key Resources

Interactive Demo — Try CCD online
GitHub Repository — Explore the CCD project on GitHub

Contrastive Decoding | Xi Zhang

🚨 Preprint out — Clinical Contrastive Decoding！

Overview

Key Resources