Radiology Workflow-Guided Hierarchical Reinforcement Fine-Tuning for Medical Report Generation

• URL: http://arxiv.org/abs/2511.10065v1
• Date: Fri, 14 Nov 2025 01:29:59 GMT
• Title: Radiology Workflow-Guided Hierarchical Reinforcement Fine-Tuning for Medical Report Generation
• Abstract summary: Radiologists compose diagnostic reports through a structured workflow.<n>Most existing medical report generation systems treat reports as flat sequences, overlooking this hierarchical organization.<n>We propose RadFlow, a hierarchical workflow-guided reinforcement framework that explicitly models the structured nature of clinical reporting.
• Score: 11.117796080402044
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Radiologists compose diagnostic reports through a structured workflow: they describe visual findings, summarize them into impressions, and carefully refine statements in clinically critical cases. However, most existing medical report generation (MRG) systems treat reports as flat sequences, overlooking this hierarchical organization and leading to inconsistencies between descriptive and diagnostic content. To align model behavior with real-world reporting practices, we propose RadFlow, a hierarchical workflow-guided reinforcement optimization framework that explicitly models the structured nature of clinical reporting. RadFlow introduces a clinically grounded reward hierarchy that mirrors the organization of radiological reports. At the global level, the reward integrates linguistic fluency, medical-domain correctness, and cross-sectional consistency between Finding and Impression, promoting coherent and clinically faithful narratives. At the local level, a section-specific reward emphasizes Impression quality, reflecting its central role in diagnostic accuracy. Furthermore, a critical-aware policy optimization mechanism adaptively regularizes learning for high-risk or clinically sensitive cases, emulating the cautious refinement behavior of radiologists when documenting critical findings. Together, these components translate the structured reporting paradigm into the reinforcement fine-tuning process, enabling the model to generate reports that are both linguistically consistent and clinically aligned. Experiments on chest X-ray and carotid ultrasound datasets demonstrate that RadFlow consistently improves diagnostic coherence and overall report quality compared with state-of-the-art baselines.

Related papers

• Concept-Grounded Reasoning with Prompt-Driven Localization for Interpretable Structured Report Generation [66.62662330558906]
  CORAL is a multimodal framework that integrates spatial grounding and concept-level supervision into a unified reasoning process.<n>CORAL employs a prompt-driven medical segmentation model to localize lesions and predicts multi-class clinical attributes through a Concept Bottleneck module.<n>Experiments on BUS-CoT and IU X-ray datasets demonstrate consistent improvements in diagnostic accuracy, concept consistency, and report quality over strong general-purpose and medical MLLMs.
  arXiv  Detail & Related papers  (2026-09-14T10:21:23Z)
• Graph-Supervised Hierarchical Clinical Alignment for Radiology Report Generation with Large Language Models [46.202482780666635]
  Graph-Hierarchicald Supervised Clinical Alignment reformulates image-report supervision as a hierarchical clinical alignment problem.<n>Our method structures this alignment as a disease-conditioned process, where supervision is decomposed into two levels.<n> Experiments on MIMIC-CXR, IU-Xray, and COV-CTR show that our method consistently improves performance on both conventional and clinical metrics.
  arXiv  Detail & Related papers  (2026-08-25T06:32:26Z)
• Structure Observation Driven Image-Text Contrastive Learning for Computed Tomography Report Generation [51.509572354327986]
  This work introduces a novel two-stage (structure- and report-learning) framework tailored for Computed Tomography Report Generation (CTRG)<n>In the first stage, a set of learnable structure-specific visual queries observe corresponding structures in a CT image. The resulting observation tokens are contrasted with structure-specific textual features extracted from the accompanying radiology report with a structure-wise image-text contrastive loss.<n>In the second stage, the visual structure queries are frozen and used to select the critical image patch embeddings depicting each anatomical structure, minimizing distractions from irrelevant areas while reducing memory consumption.
  arXiv  Detail & Related papers  (2026-03-05T07:07:07Z)
• AgentsEval: Clinically Faithful Evaluation of Medical Imaging Reports via Multi-Agent Reasoning [73.50200033931148]
  We introduce AgentsEval, a multi-agent stream reasoning framework that emulates the collaborative diagnostic workflow of radiologists.<n>By dividing the evaluation process into interpretable steps including criteria definition, evidence extraction, alignment, and consistency scoring, AgentsEval provides explicit reasoning traces and structured clinical feedback.<n> Experimental results demonstrate that AgentsEval delivers clinically aligned, semantically faithful, and interpretable evaluations that remain robust under paraphrastic, semantic, and stylistic perturbations.
  arXiv  Detail & Related papers  (2026-01-23T11:59:13Z)
• Aligning Findings with Diagnosis: A Self-Consistent Reinforcement Learning Framework for Trustworthy Radiology Reporting [37.57009831483529]
  Multimodal Large Language Models (MLLMs) have shown strong potential for radiology report generation.<n>Our framework restructures generation into two distinct components: a think block for detailed findings and an answer block for structured disease labels.
  arXiv  Detail & Related papers  (2026-01-06T14:17:44Z)
• Self-Supervised Anatomical Consistency Learning for Vision-Grounded Medical Report Generation [61.350584471060756]
  Vision-grounded medical report generation aims to produce clinically accurate descriptions of medical images.<n>We propose Self-Supervised Anatomical Consistency Learning (SS-ACL) to align generated reports with corresponding anatomical regions.<n>SS-ACL constructs a hierarchical anatomical graph inspired by the invariant top-down inclusion structure of human anatomy.
  arXiv  Detail & Related papers  (2025-09-30T08:59:06Z)
• S-RRG-Bench: Structured Radiology Report Generation with Fine-Grained Evaluation Framework [39.542375803362965]
  Radiology report generation (RRG) for diagnostic images, such as chest X-rays, plays a pivotal role in both clinical practice and AI.<n>Traditional free-text reports suffer from redundancy and inconsistent language, complicating the extraction of critical clinical details.<n>We present a novel approach to S-RRG that includes dataset construction, model training, and the introduction of a new evaluation framework.
  arXiv  Detail & Related papers  (2025-08-04T05:49:41Z)
• RadFabric: Agentic AI System with Reasoning Capability for Radiology [61.25593938175618]
  RadFabric is a multi agent, multimodal reasoning framework that unifies visual and textual analysis for comprehensive CXR interpretation.<n>System employs specialized CXR agents for pathology detection, an Anatomical Interpretation Agent to map visual findings to precise anatomical structures, and a Reasoning Agent powered by large multimodal reasoning models to synthesize visual, anatomical, and clinical data into transparent and evidence based diagnoses.
  arXiv  Detail & Related papers  (2025-06-17T03:10:33Z)
• Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation [10.46031380503486]
  We introduce a novel method, textbfStructural textbfEntities extraction and patient indications textbfIncorporation (SEI) for chest X-ray report generation. We employ a structural entities extraction (SEE) approach to eliminate presentation-style vocabulary in reports. We propose a cross-modal fusion network to integrate information from X-ray images, similar historical cases, and patient-specific indications.
  arXiv  Detail & Related papers  (2024-05-23T01:29:47Z)
• HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction [16.060286162384536]
  HistGen is a learning-empowered framework for histopathology report generation. It aims to boost report generation by aligning whole slide images (WSIs) and diagnostic reports from local and global granularity. Experimental results on WSI report generation show the proposed model outperforms state-of-the-art (SOTA) models by a large margin.
  arXiv  Detail & Related papers  (2024-03-08T15:51:43Z)
• Radiology Report Generation Using Transformers Conditioned with Non-imaging Data [55.17268696112258]
  This paper proposes a novel multi-modal transformer network that integrates chest x-ray (CXR) images and associated patient demographic information. The proposed network uses a convolutional neural network to extract visual features from CXRs and a transformer-based encoder-decoder network that combines the visual features with semantic text embeddings of patient demographic information.
  arXiv  Detail & Related papers  (2023-11-18T14:52:26Z)
• Beyond Images: An Integrative Multi-modal Approach to Chest X-Ray Report Generation [47.250147322130545]
  Image-to-text radiology report generation aims to automatically produce radiology reports that describe the findings in medical images. Most existing methods focus solely on the image data, disregarding the other patient information accessible to radiologists. We present a novel multi-modal deep neural network framework for generating chest X-rays reports by integrating structured patient data, such as vital signs and symptoms, alongside unstructured clinical notes.
  arXiv  Detail & Related papers  (2023-11-18T14:37:53Z)
• ChatRadio-Valuer: A Chat Large Language Model for Generalizable Radiology Report Generation Based on Multi-institution and Multi-system Data [115.0747462486285]
  ChatRadio-Valuer is a tailored model for automatic radiology report generation that learns generalizable representations. The clinical dataset utilized in this study encompasses a remarkable total of textbf332,673 observations. ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al.
  arXiv  Detail & Related papers  (2023-10-08T17:23:17Z)
• FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays [37.15474283789249]
  We propose a method to predict clinical findings defined by sentences in structured reporting templates. The approach involves training a contrastive language-image model using chest X-rays and related free-text radiological reports. Results show that even with limited image-level annotations for training, the method can accomplish the structured reporting tasks of severity assessment of cardiomegaly and localizing pathologies in chest X-rays.
  arXiv  Detail & Related papers  (2022-03-29T16:31:39Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.