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From Sampled Evidence to Radiology Reports: Objectives, Constraints, and Realization

arXiv 2610.17318 ↗First posted 12 Oct 2026cs.CLcited by 0

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Abstract (arXiv)

Sampling multiple reports creates an inference problem: how should sampled evidence be converted into a final decision, and does a better structured decision yield a better report? We study these questions through a framework that separates the decision signal, scoring objective, and feasible decision set, followed by text realization. On MIMIC-CXR and IU-Xray, controlled comparisons across 13 generator configurations per benchmark under grouped development/test evaluation reveal three findings. First, changing the objective under fixed evidence and rule capacity changes predictions and their held-out metric trade-offs. Second, relaxing candidate constraints has objective-dependent effects under shared signals and frozen scores. Diagnostics connect the IU macro-oriented assertion increase to near-zero label thresholds and show that limited candidate coverage coexists with selection headroom. Third, a controlled realization study with SFT-Qwen3-VL-8B shows that decision-level gains can be attenuated, amplified, or reversed. Change decomposition reveals that realization can remove incorrect assertions while losing correct findings, so target fidelity and reference accuracy need not improve together. Finding gains also do not consistently improve reference-based report quality. These results motivate separate evaluation of decision rules, candidate constraints, and realized reports.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 24 of its 41 references that are in the map. First posted on arXiv 12 Oct 2026.

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References · 24

  1. 2021CLIPScore: A Reference-free Evaluation Metric for Image Captioningcited by 2,424
  2. 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,423
  3. 2020CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTcited by 337
  4. 2021RadGraph: Extracting Clinical Entities and Relations from Radiology Reportscited by 319
  5. 2020Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generationcited by 141
  6. 2024GREEN: Generative Radiology Report Evaluation and Error Notationcited by 128
  7. 2022Improving the Factual Correctness of Radiology Report Generation with Semantic Rewardscited by 83
  8. 2021Sampling-Based Approximations to Minimum Bayes Risk Decoding for Neural Machine Translationcited by 65
  9. 2023Epsilon Sampling Rocks: Investigating Sampling Strategies for Minimum Bayes Risk Decoding for Machine Translationcited by 58
  10. 2023It's MBR All the Way Down: Modern Generation Techniques Through the Lens of Minimum Bayes Riskcited by 51
  11. 2025RadEval: A framework for radiology text evaluationcited by 22
  12. 2015Optimizing Non-decomposable Performance Measures: A Tale of Two Classescited by 18
12 more
  1. 2013On the Bayes-optimality of F-measure maximizerscited by 16
  2. 2024Mitigating Metric Bias in Minimum Bayes Risk Decodingcited by 16
  3. 2023Model-Based Minimum Bayes Risk Decoding for Text Generationcited by 11
  4. 2025Uncertainty-Aware Decoding with Minimum Bayes Riskcited by 11
  5. 2024X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluationcited by 10
  6. 2024Diversity Explains Inference Scaling Laws: Through a Case Study of Minimum Bayes Risk Decodingcited by 9
  7. 2024Semantic Consistency-Based Uncertainty Quantification for Factuality in Radiology Report Generationcited by 5
  8. 2024Reranking Laws for Language Generation: A Communication-Theoretic Perspectivecited by 4
  9. 2025RA-RRG: Multimodal Retrieval-Augmented Radiology Report Generation with Key Phrase Extractioncited by 4
  10. 2025EditGRPO: Reinforcement Learning with Post-Rollout Edits for Clinically Accurate Chest X-Ray Report Generationcited by 4
  11. 2026SDR: Set-Distance Rewards for Radiology Report Generationcited by 3
  12. 2025Structure-Conditional Minimum Bayes Risk Decodingcited by 2

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