CCS：放射学报告生成的临床共识选择
CCS: Clinical Consensus Selection for Radiology Report Generation
AI总结：
提出CCS框架，通过采样多个候选报告并选择临床共识最高的一个，以改进放射学报告生成在推理时的质量。
摘要：
放射学报告生成（RRG）通常被表述为单路径生成任务，其中多模态大语言模型（MLLM）产生一个解码报告作为最终输出。虽然最近的进展主要通过扩展训练数据、模型容量和检索机制来推动，但在推理时提高报告质量仍未被充分探索。在这项工作中，我们观察到固定的放射学MLLM在其候选池中通常生成比默认解码选择的报告临床更强的报告，这表明推理时的决策仍然是一个被忽视的瓶颈。为了解决这个问题，我们提出了临床共识选择（CCS），一个解码器无关的推理时选择框架，它采样多个候选报告，并选择在展开池中具有最高临床共识的报告。CCS将基于文本的效用与由图像-报告训练的多模态嵌入器计算的放射学适应效用统一起来，该嵌入器测量超越表面文本相似性的候选一致性。在三个数据集和多个放射学MLLM上，CCS始终优于单路径解码和通用Best-of-N基线，特别是在临床指标上取得了明显提升。进一步分析表明，基于图像的效用形成了与文本共识不同的选择轴，并且在推理时改进RRG仍有很大的提升空间。
英文摘要：
Radiology report generation (RRG) is commonly formulated as a single-path generation task, where a multimodal large language model (MLLM) produces one decoded report as the final output. While recent progress has largely been driven by scaling training data, model capacity, and retrieval mechanisms, improving report quality at inference time remains underexplored. In this work, 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 remains 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 unifies text-based utilities with a radiology-adapted utility computed by an image--report-trained multimodal embedder, which measures candidate 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 particularly 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
