CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical
  Contrastive Decoding
Multimodal large language models (MLLMs) have recently achieved remarkable
progress in radiology by integrating visual perception with natural language
understanding. However, they often generate clinically unsupported
descriptions, known as medical hallucinations, which pose serious risks in
medical applications that demand accuracy and image-grounded outputs. Through
empirical analysis, we find that prompt-induced hallucinations remain prevalent
in radiology MLLMs, largely due to over-sensitivity to clinical sections. To
address this, we introduce Clinical Contrastive Cecoding (CCD), a training-free
and retrieval-free inference framework that integrates structured clinical
signals from task-specific radiology expert models. CCD introduces a dual-stage
contrastive mechanism to refine token-level logits during generation, thereby
enhancing clinical fidelity without modifying the base MLLM. Experiments on
three datasets and multiple models demonstrate that CCD consistently improves
overall performance on radiology report generation (RRG). On the MIMIC-CXR
dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to
state-of-the-art RRG models. Our approach provides a lightweight and
generalisable solution for mitigating medical hallucinations, effectively
bridging expert models and MLLMs in radiology.
68dbdbea4159d1f2418f975b
Xi Zhang
claimed_verified
2025-10-01T10:24:22.580Z
652ebdcc76365388909b06cf
https://cdn-avatars.huggingface.co/v1/production/uploads/652ebdcc76365388909b06cf/MnIVVuTphX8buEER9EkCY.jpeg
Xi Zhang
user
X-iZhang
68dbdbea4159d1f2418f975c
Zaiqiao Meng
68dbdbea4159d1f2418f975d
Jake Lever
68dbdbea4159d1f2418f975e
Edmond S. L. Ho
CCD: Mitigating Hallucinations in Radiology MLLMs via Clinical
  Contrastive Decoding
Multimodal large language models (MLLMs) have recently achieved remarkable
progress in radiology by integrating visual perception with natural language
understanding. However, they often generate clinically unsupported
descriptions, known as medical hallucinations, which pose serious risks in
medical applications that demand accuracy and image-grounded outputs. Through
empirical analysis, we find that prompt-induced hallucinations remain prevalent
in radiology MLLMs, largely due to over-sensitivity to clinical sections. To
address this, we introduce Clinical Contrastive Cecoding (CCD), a training-free
and retrieval-free inference framework that integrates structured clinical
signals from task-specific radiology expert models. CCD introduces a dual-stage
contrastive mechanism to refine token-level logits during generation, thereby
enhancing clinical fidelity without modifying the base MLLM. Experiments on
three datasets and multiple models demonstrate that CCD consistently improves
overall performance on radiology report generation (RRG). On the MIMIC-CXR
dataset, it yields up to a 17% improvement in RadGraph-F1 when applied to
state-of-the-art RRG models. Our approach provides a lightweight and
generalisable solution for mitigating medical hallucinations, effectively
bridging expert models and MLLMs in radiology.
Clinical Contrastive Cecoding (CCD) enhances radiology report generation by integrating structured clinical signals, reducing medical hallucinations without altering the base MLLM.
multimodal large language models MLLMs radiology visual perception natural language understanding medical hallucinations prompt-induced hallucinations clinical sections Clinical Contrastive Cecoding CCD dual-stage contrastive mechanism token-level logits radiology report generation RRG MIMIC-CXR dataset RadGraph-F1