# KiUT: Knowledge-injected U-Transformer for Radiology Report Generation

arXiv: https://arxiv.org/abs/2306.11345 · HTML: https://mlsanity.com/paper/2306.11345/

## Key facts

- Cited by 62 papers in the map (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 20 Jun 2023.

## Builds on (most cited first, 30 of 36)

- [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)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Show, Attend and Tell: Neural Image Caption Generation with Visual Attention](https://mlsanity.com/paper/1502.03044/) (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)
- [Meshed-Memory Transformer for Image Captioning](https://mlsanity.com/paper/1912.08226/) (2019)
- [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)
- [Image Captioning: Transforming Objects into Words](https://mlsanity.com/paper/1906.05963/) (2019)
- [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://mlsanity.com/paper/2106.06963/) (2021)
- [When Radiology Report Generation Meets Knowledge Graph](https://mlsanity.com/paper/2002.08277/) (2020)
- [Hybrid Retrieval-Generation Reinforced Agent for Medical Image Report Generation](https://mlsanity.com/paper/1805.08298/) (2018)
- [Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/1904.02633/) (2019)
- [TorchXRayVision: A library of chest X-ray datasets and models](https://mlsanity.com/paper/2111.00595/) (2021)
- [Show and Tell: Lessons learned from the 2015 MSCOCO Image Captioning Challenge](https://mlsanity.com/paper/1609.06647/) (2016)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [Show, Control and Tell: A Framework for Generating Controllable and Grounded Captions](https://mlsanity.com/paper/1811.10652/) (2018)
- [AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation](https://mlsanity.com/paper/2203.10095/) (2022)
- [Normalized and Geometry-Aware Self-Attention Network for Image Captioning](https://mlsanity.com/paper/2003.08897/) (2020)
- [Automatic Radiology Report Generation based on Multi-view Image Fusion and Medical Concept Enrichment](https://mlsanity.com/paper/1907.09085/) (2019)
- [Cross-modal Prototype Driven Network for Radiology Report Generation](https://mlsanity.com/paper/2207.04818/) (2022)

## Built on by (most cited first, 30 of 62)

- [R2GenGPT: Radiology Report Generation with Frozen LLMs](https://mlsanity.com/paper/2309.09812/) (2023)
- [PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation](https://mlsanity.com/paper/2308.12604/) (2023)
- [Automated Radiology Report Generation: A Review of Recent Advances](https://mlsanity.com/paper/2405.10842/) (2024)
- [Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation](https://mlsanity.com/paper/2502.20056/) (2025)
- [CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset](https://mlsanity.com/paper/2410.00379/) (2024)
- [HERGen: Elevating Radiology Report Generation with Longitudinal Data](https://mlsanity.com/paper/2407.15158/) (2024)
- [RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning](https://mlsanity.com/paper/2310.13864/) (2023)
- [Complex Organ Mask Guided Radiology Report Generation](https://mlsanity.com/paper/2311.02329/) (2023)
- [LLM-driven Multimodal Target Volume Contouring in Radiation Oncology](https://mlsanity.com/paper/2311.01908/) (2023)
- [Self-eXplainable AI for Medical Image Analysis: A Survey and New Outlooks](https://mlsanity.com/paper/2410.02331/) (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)
- [R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation](https://mlsanity.com/paper/2408.09743/) (2024)
- [Factual Serialization Enhancement: A Key Innovation for Chest X-ray Report Generation](https://mlsanity.com/paper/2405.09586/) (2024)
- [MedRAT: Unpaired Medical Report Generation via Auxiliary Tasks](https://mlsanity.com/paper/2407.03919/) (2024)
- [Taming Vision-Language Models for Medical Image Analysis: A Comprehensive Review](https://mlsanity.com/paper/2506.18378/) (2025)
- [Dragonfly: Multi-Resolution Zoom-In Encoding Enhances Vision-Language Models](https://mlsanity.com/paper/2406.00977/) (2024)
- [DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation](https://mlsanity.com/paper/2505.03401/) (2025)
- [CT-GRAPH: Hierarchical Graph Attention Network for Anatomy-Guided CT Report Generation](https://mlsanity.com/paper/2508.05375/) (2025)
- [Pre-training on High Definition X-ray Images: An Experimental Study](https://mlsanity.com/paper/2404.17926/) (2024)
- [A Chain of Diagnosis Framework for Accurate and Explainable Radiology Report Generation](https://mlsanity.com/paper/2508.09566/) (2025)
- [Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset](https://mlsanity.com/paper/2508.10528/) (2025)
- [Prompt-Guided Generation of Structured Chest X-Ray Report Using a Pre-trained LLM](https://mlsanity.com/paper/2404.11209/) (2024)
- [A Survey of Deep Learning-based Radiology Report Generation Using Multimodal Data](https://mlsanity.com/paper/2405.12833/) (2024)
- [Visual Alignment of Medical Vision-Language Models for Grounded Radiology Report Generation](https://mlsanity.com/paper/2512.16201/) (2025)
- [EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge](https://mlsanity.com/paper/2411.10224/) (2024)
- [MvKeTR: Chest CT Report Generation with Multi-View Perception and Knowledge Enhancement](https://mlsanity.com/paper/2411.18309/) (2024)
- [LLM-driven Medical Report Generation via Communication-efficient Heterogeneous Federated Learning](https://mlsanity.com/paper/2506.17562/) (2025)
- [TiBiX: Leveraging Temporal Information for Bidirectional X-ray and Report Generation](https://mlsanity.com/paper/2403.13343/) (2024)
- [Image-aware Evaluation of Generated Medical Reports](https://mlsanity.com/paper/2410.17357/) (2024)

## Abstract (arXiv)

Radiology report generation aims to automatically generate a clinically accurate and coherent paragraph from the X-ray image, which could relieve radiologists from the heavy burden of report writing. Although various image caption methods have shown remarkable performance in the natural image field, generating accurate reports for medical images requires knowledge of multiple modalities, including vision, language, and medical terminology. We propose a Knowledge-injected U-Transformer (KiUT) to learn multi-level visual representation and adaptively distill the information with contextual and clinical knowledge for word prediction. In detail, a U-connection schema between the encoder and decoder is designed to model interactions between different modalities. And a symptom graph and an injected knowledge distiller are developed to assist the report generation. Experimentally, we outperform state-of-the-art methods on two widely used benchmark datasets: IU-Xray and MIMIC-CXR. Further experimental results prove the advantages of our architecture and the complementary benefits of the injected knowledge.