🚨 Preprint out — Clinical Consensus Selection！

Fri, 29 May 2026 00:00:00 +0000

“CCS: Clinical Consensus Selection for Radiology Report Generation”. The preprint is now available.

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

Overview

Radiology report generation (RRG) is usually treated as a single-path task: a multimodal large language model (MLLM) emits one decoded report and commits to it. Yet a fixed model often places clinically stronger reports elsewhere in its candidate pool than the one chosen by default decoding—so inference-time decision making remains an overlooked bottleneck. We introduce Clinical Consensus Selection (CCS), a decoder-agnostic, reference-free framework that samples multiple candidate reports and selects the one with the highest clinical consensus across the rollout pool. CCS unifies text-based utilities with a radiology-adapted utility from an image–report-trained multimodal embedder, measuring candidate agreement beyond surface-level text. Across three datasets and multiple radiology MLLMs, CCS consistently improves over single-path decoding and generic Best-of-N baselines, with particularly clear gains on clinical metrics.

Key Resources

GitHub Repository — Explore the CCS project on GitHub

CCS: Clinical Consensus Selection for Radiology Report Generation

Fri, 29 May 2026 00:00:00 +0000

For the latest updates and details, visit the CCS Project Website.

Why CCS? 🔬

Most radiology MLLMs commit to a single decoded report token by token, so one unfavourable step can omit a finding or assert one unsupported by the image, with no way to recover. Yet a fixed model often places clinically stronger reports elsewhere in its candidate pool — the bottleneck is not what the model can generate, but which candidate it commits to.

How CCS Works ⚙️

Clinical Consensus Selection (CCS) is a reference-free, decoder-agnostic inference-time framework that reframes RRG as candidate selection — no retraining or extra parameters.

Rollout sampling: draw N candidate reports from the radiology MLLM via stochastic decoding.
Textual consensus utility: repurpose report-evaluation metrics as reference-free pairwise agreement scores.
Image-grounded utility: measure candidate agreement with Qwen3-VL-Embed, a multimodal embedder adapted on image–report pairs, capturing clinical agreement beyond surface text.
Highest-consensus selection: return the candidate with the highest mean pairwise consensus over the pool.

Key Contributions ✨

Inference-time perspective: We show candidate pools routinely contain reports with higher clinical reliability and consistency than single-path outputs.
CCS framework: A decoder-agnostic Best-of-N method that aggregates pairwise clinical consensus using both textual and image–report-adapted multimodal utilities.
Consistent gains: Across three datasets and multiple radiology MLLMs, CCS improves RRG at inference time and identifies image-grounded utility as a distinct selection axis.

Main Results 📈

On MIMIC-CXR, CCS improves all radiology-specific metrics over Sampling (e.g. RadGraph-F1 0.1989 → 0.2134, CheXbert-F1⁵ 0.5041 → 0.5370), with gains statistically significant (p < 0.05). Unlike generic Best-of-N selectors (Self-Certainty, ModeX) that give inconsistent or negative clinical gains, CCS improves consistently across all backbones and datasets.

Analysis 📊

Per-label F1 shows CCS recovers abnormal findings that text-only consensus suppresses — confirming that image-grounded utility is a selection axis distinct from textual consensus, with substantial headroom still remaining.

Case Study 🔍

On a real MIMIC-CXR case, single-path decoding asserts factual errors (false “clear lungs”, mislocalised catheter), whereas CCS selects a more image-grounded report (CheXbert-F1⁵ 1.0000 vs Sampling 0.5000).

BibTeX 📚

@article{zhang2026ccsclinicalconsensusselection,
 title={CCS: Clinical Consensus Selection for Radiology Report Generation},
 author={Xi Zhang and Yingshu Li and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
 year={2026},
 eprint={2605.30131},
 archivePrefix={arXiv},
 primaryClass={cs.CL},
 url={https://arxiv.org/abs/2605.30131},
}

Best-of-N Decoding | Xi Zhang