# ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning

arXiv: https://arxiv.org/abs/2306.06466 · HTML: https://mlsanity.com/paper/2306.06466/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 59 references that are in the map.
- First posted on arXiv 10 Jun 2023.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [Bottom-Up and Top-Down Attention for Image Captioning and Visual Question Answering](https://mlsanity.com/paper/1707.07998/) (2017)
- [Show and Tell: A Neural Image Caption Generator](https://mlsanity.com/paper/1411.4555/) (2014)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Self-critical Sequence Training for Image Captioning](https://mlsanity.com/paper/1612.00563/) (2016)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [On the Automatic Generation of Medical Imaging Reports](https://mlsanity.com/paper/1711.08195/) (2017)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation](https://mlsanity.com/paper/1805.08298/) (2018)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)
- [Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/1904.02633/) (2019)
- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [Step-by-Step: Separating Planning from Realization in Neural Data-to-Text Generation](https://mlsanity.com/paper/1904.03396/) (2019)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation](https://mlsanity.com/paper/2203.10095/) (2022)
- [Plan-then-Generate: Controlled Data-to-Text Generation via Planning](https://mlsanity.com/paper/2108.13740/) (2021)
- [Progressive Transformer-Based Generation of Radiology Reports](https://mlsanity.com/paper/2102.09777/) (2021)
- [PAIR: Planning and Iterative Refinement in Pre-trained Transformers for Long Text Generation](https://mlsanity.com/paper/2010.02301/) (2020)
- [PLANET: Dynamic Content Planning in Autoregressive Transformers for Long-form Text Generation](https://mlsanity.com/paper/2203.09100/) (2022)
- [Prototype-to-Style: Dialogue Generation with Style-Aware Editing on Retrieval Memory](https://mlsanity.com/paper/2004.02214/) (2020)
- [Plan ahead: Self-Supervised Text Planning for Paragraph Completion Task](https://mlsanity.com/paper/2010.05141/) (2020)

## Built on by (most cited first, 30 of 42)

- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset](https://mlsanity.com/paper/2410.00379/) (2024)
- [Libra: Leveraging Temporal Images for Biomedical Radiology Analysis](https://mlsanity.com/paper/2411.19378/) (2024)
- [RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning](https://mlsanity.com/paper/2310.13864/) (2023)
- [Has Multimodal Learning Delivered Universal Intelligence in Healthcare? A Comprehensive Survey](https://mlsanity.com/paper/2408.12880/) (2024)
- [Overview of the First Shared Task on Clinical Text Generation: RRG24 and "Discharge Me!"](https://mlsanity.com/paper/2409.16603/) (2024)
- [ChEX: Interactive Localization and Region Description in Chest X-rays](https://mlsanity.com/paper/2404.15770/) (2024)
- [A Systematic Review of Deep Learning-based Research on Radiology Report Generation](https://mlsanity.com/paper/2311.14199/) (2023)
- [Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generation](https://mlsanity.com/paper/2501.03458/) (2025)
- [RADAR: Enhancing Radiology Report Generation with Supplementary Knowledge Injection](https://mlsanity.com/paper/2505.14318/) (2025)
- [X-ray Made Simple: Lay Radiology Report Generation and Robust Evaluation](https://mlsanity.com/paper/2406.17911/) (2024)
- [DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation](https://mlsanity.com/paper/2505.03401/) (2025)
- [Pre-training on High Definition X-ray Images: An Experimental Study](https://mlsanity.com/paper/2404.17926/) (2024)
- [A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data](https://mlsanity.com/paper/2405.12833/) (2024)
- [A Disease-Aware Dual-Stage Framework for Chest X-ray Report Generation](https://mlsanity.com/paper/2511.12259/) (2025)
- [EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge](https://mlsanity.com/paper/2411.10224/) (2024)
- [DAMPER: A Dual-Stage Medical Report Generation Framework with Coarse-Grained MeSH Alignment and Fine-Grained Hypergraph Matching](https://mlsanity.com/paper/2412.14535/) (2024)
- [R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation](https://mlsanity.com/paper/2508.03426/) (2025)
- [Rethinking Radiology Report Generation via Causal Inspired Counterfactual Augmentation](https://mlsanity.com/paper/2311.13307/) (2023)
- [Image-aware Evaluation of Generated Medical Reports](https://mlsanity.com/paper/2410.17357/) (2024)
- [ORID: Organ-Regional Information Driven Framework for Radiology Report Generation](https://mlsanity.com/paper/2411.13025/) (2024)
- [EMRRG: Efficient Fine-Tuning Pre-trained X-ray Mamba Networks for Radiology Report Generation](https://mlsanity.com/paper/2510.16776/) (2025)
- [ICON: Improving Inter-Report Consistency in Radiology Report Generation via Lesion-aware Mixup Augmentation](https://mlsanity.com/paper/2402.12844/) (2024)
- [MedCycle: Unpaired Medical Report Generation via Cycle-Consistency](https://mlsanity.com/paper/2403.13444/) (2024)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [Dynamic Traceback Learning for Medical Report Generation](https://mlsanity.com/paper/2401.13267/) (2024)
- [CLARIFID: Improving Radiology Report Generation by Reinforcing Clinically Accurate Impressions and Enforcing Detailed Findings](https://mlsanity.com/paper/2507.17234/) (2025)
- [OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation](https://mlsanity.com/paper/2509.18600/) (2025)
- [MRI2Rep: Autoregressive Structured Report Generation for 3D Liver MRI](https://mlsanity.com/paper/2606.25279/) (2026)
- [Seeing What Matters: Lesion-Aware High-Resolution Patch Discovery and Fusion for Chest X-ray Report Generation](https://mlsanity.com/paper/2607.06909/) (2026)

## Abstract (arXiv)

This paper explores the task of radiology report generation, which aims at generating free-text descriptions for a set of radiographs. One significant challenge of this task is how to correctly maintain the consistency between the images and the lengthy report. Previous research explored solving this issue through planning-based methods, which generate reports only based on high-level plans. However, these plans usually only contain the major observations from the radiographs (e.g., lung opacity), lacking much necessary information, such as the observation characteristics and preliminary clinical diagnoses. To address this problem, the system should also take the image information into account together with the textual plan and perform stronger reasoning during the generation process. In this paper, we propose an observation-guided radiology report generation framework (ORGAN). It first produces an observation plan and then feeds both the plan and radiographs for report generation, where an observation graph and a tree reasoning mechanism are adopted to precisely enrich the plan information by capturing the multi-formats of each observation. Experimental results demonstrate that our framework outperforms previous state-of-the-art methods regarding text quality and clinical efficacy