mlsanityData to 14 Oct 2026

PACER: Progressive Availability-Conditioned Evidence Routing for Radiology Report Generation under Incomplete Clinical Context

arXiv 2609.34487 ↗First posted 28 Sep 2026cs.CVcited by 0

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Abstract (arXiv)

Radiology report generation (RRG) increasingly incorporates heterogeneous clinical evidence, such as multi-view radiographs and previous reports, whose availability varies across examinations. However, accommodating different input combinations does not ensure effective evidence use: generated reports may still omit or inaccurately describe clinically relevant findings. To address this problem, we propose PACER, a Progressive Availability-Conditioned Evidence Routing framework for structured incomplete-context RRG that follows a Refine-Calibrate-Commit pipeline. It first refines observed visual representations through endpoint-preserving patchwise routing across frozen encoder depths, incorporating complementary cues while retaining the pretrained terminal representation. It then calibrates the language-model prefix according to the observed evidence and availability state, adapting the shared generator's conditioning as the available source set changes. Finally, it generates polarity-structured clinical commitments before the report in the same autoregressive trajectory, providing structured clinical context for subsequent generation. Experiments demonstrate state-of-the-art clinical efficacy across all four MIMIC-RG4 settings and strong MIMIC-CXR performance, while maintaining competitive language-generation quality.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 31 of its 58 references that are in the map. First posted on arXiv 28 Sep 2026.

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References · 31

  1. 2021Vision Transformers for Dense Predictioncited by 1,803
  2. 2020Generating Radiology Reports via Memory-driven Transformercited by 368
  3. 2022Making the Most of Text Semantics to Improve Biomedical Vision--Language Processingcited by 339
  4. 2023Towards Generalist Biomedical AIcited by 289
  5. 2022Cross-modal Memory Networks for Radiology Report Generationcited by 185
  6. 2023Interactive and Explainable Region-guided Radiology Report Generationcited by 140
  7. 2024Exploring scalable medical image encoders beyond text supervisioncited by 132
  8. 2023R2GenGPT: Radiology Report Generation with Frozen LLMscited by 114
  9. 2022Improving Chest X-Ray Report Generation by Leveraging Warm Startingcited by 95
  10. 2023PromptMRG: Diagnosis-Driven Prompts for Medical Report Generationcited by 95
  11. 2024A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretationcited by 81
  12. 2023KiUT: Knowledge-injected U-Transformer for Radiology Report Generationcited by 62
19 more
  1. 2023M3AE: Multimodal Representation Learning for Brain Tumor Segmentation with Missing Modalitiescited by 56
  2. 2024DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistencycited by 53
  3. 2023ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoningcited by 42
  4. 2025Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generationcited by 42
  5. 2024CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Datasetcited by 35
  6. 2025SimMLM: A Simple Framework for Multi-modal Learning with Missing Modalitycited by 23
  7. 2025Multi-Layer Visual Feature Fusion in Multimodal LLMs: Methods, Analysis, and Best Practicescited by 22
  8. 2025Retrieval-Augmented Dynamic Prompt Tuning for Incomplete Multimodal Learningcited by 15
  9. 2024LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input Contextscited by 13
  10. 2024Gradient-Guided Modality Decoupling for Missing-Modality Robustnesscited by 11
  11. 2024HC-LLM: Historical-Constrained Large Language Models for Radiology Report Generationcited by 9
  12. 2025PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generationcited by 9
  13. 2026A Reasoning-Enabled Vision-Language Foundation Model for Chest X-ray Interpretationcited by 5
  14. 2025Learnable Retrieval Enhanced Visual-Text Alignment and Fusion for Radiology Report Generationcited by 4
  15. 2025S2D-ALIGN: Shallow-to-Deep Auxiliary Learning for Anatomically-Grounded Radiology Report Generationcited by 4
  16. 2026Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learningcited by 3
  17. 2026AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generationcited by 2
  18. 2025DiA-gnostic VLVAE: Disentangled Alignment-Constrained Vision Language Variational AutoEncoder for Robust Radiology Reporting with Missing Modalitiescited by 1
  19. 2026CheXmix: Unified Generative Pretraining for Vision Language Models in Medical Imagingcited by 1

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