🎤 Talk at Glasgow AI4BioMed Lab!

Wed, 17 Jun 2026 00:00:00 +0000

💡 Talk Title

“CCS: Clinical Consensus Selection for Radiology Report Generation”

🖇️ Abstract

Radiology report generation (RRG) is commonly framed as a single-path task, where a multimodal large language model (MLLM) produces one decoded report as the final output. While progress has largely come from scaling training data, model capacity, and retrieval mechanisms, improving report quality at inference time remains underexplored. We observe that fixed radiology MLLMs often generate clinically stronger reports elsewhere in their candidate pool than the one selected by default decoding, suggesting that inference-time decision making is an overlooked bottleneck. To address this, we propose Clinical Consensus Selection (CCS), a decoder-agnostic inference-time selection framework that samples multiple candidate reports and selects the one with the highest clinical consensus across the rollout pool. CCS combines text-based utilities with a radiology-adapted utility computed by an image–report-trained multimodal embedder, measuring agreement beyond surface-level textual similarity. Across three datasets and multiple radiology MLLMs, CCS consistently improves inference-time performance over single-path decoding and generic Best-of-N baselines, with especially clear gains on clinical metrics. Further analysis shows that image-grounded utility forms a selection axis distinct from textual consensus, and that substantial headroom remains for improving RRG at inference time.

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

📺 Slides

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

📅

When: Wednesday, June 17, 2026 at 2pm
Where: F121

See you there!

🎤 Talk at Glasgow AI4BioMed Lab!

Wed, 28 Jan 2026 00:00:00 +0000

💡 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.

📅

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

See you there!

AI4BioMed | Xi Zhang

🎤 Talk at Glasgow AI4BioMed Lab!

💡 Talk Title

🖇️ Abstract

📺 Slides

📅

🎤 Talk at Glasgow AI4BioMed Lab!

💡 Talk Title

🖇️ Abstract

📺 Slides

📅