🚨 Preprint out — Clinical Consensus Selection！

“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

• Interactive Demo — Try CCS online
• GitHub Repository — Explore the CCS project on GitHub