# Dynamic Multi-Domain Knowledge Networks for Chest X-ray Report Generation

arXiv: https://arxiv.org/abs/2310.05119 · HTML: https://mlsanity.com/paper/2310.05119/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 25 of its 37 references that are in the map.
- First posted on arXiv 8 Oct 2023.

## Builds on (most cited first, 25 of 25)

- [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)
- [Semi-Supervised Classification with Graph Convolutional Networks](https://mlsanity.com/paper/1609.02907/) (2016)
- [Layer Normalization](https://mlsanity.com/paper/1607.06450/) (2016)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [Contrastive Learning of Medical Visual Representations from Paired Images and Text](https://mlsanity.com/paper/2010.00747/) (2020)
- [MIMIC-CXR-JPG, a large publicly available database of labeled chest radiographs](https://mlsanity.com/paper/1901.07042/) (2019)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [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)
- [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)
- [Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation](https://mlsanity.com/paper/2010.10042/) (2020)
- [Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation](https://mlsanity.com/paper/2303.10323/) (2023)
- [Competence-based Multimodal Curriculum Learning for Medical Report Generation](https://mlsanity.com/paper/2206.14579/) (2022)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [Knowledge Matters: Radiology Report Generation with General and Specific Knowledge](https://mlsanity.com/paper/2112.15009/) (2021)
- [AlignTransformer: Hierarchical Alignment of Visual Regions and Disease Tags for Medical Report Generation](https://mlsanity.com/paper/2203.10095/) (2022)
- [RATCHET: Medical Transformer for Chest X-ray Diagnosis and Reporting](https://mlsanity.com/paper/2107.02104/) (2021)
- [Automated Generation of Accurate & Fluent Medical X-ray Reports](https://mlsanity.com/paper/2108.12126/) (2021)
- [Representative Image Feature Extraction via Contrastive Learning Pretraining for Chest X-ray Report Generation](https://mlsanity.com/paper/2209.01604/) (2022)
- [Prior Knowledge Enhances Radiology Report Generation](https://mlsanity.com/paper/2201.03761/) (2022)

## Abstract (arXiv)

The automated generation of radiology diagnostic reports helps radiologists make timely and accurate diagnostic decisions while also enhancing clinical diagnostic efficiency. However, the significant imbalance in the distribution of data between normal and abnormal samples (including visual and textual biases) poses significant challenges for a data-driven task like automatically generating diagnostic radiology reports. Therefore, we propose a Dynamic Multi-Domain Knowledge(DMDK) network for radiology diagnostic report generation. The DMDK network consists of four modules: Chest Feature Extractor(CFE), Dynamic Knowledge Extractor(DKE), Specific Knowledge Extractor(SKE), and Multi-knowledge Integrator(MKI) module. Specifically, the CFE module is primarily responsible for extracting the unprocessed visual medical features of the images. The DKE module is responsible for extracting dynamic disease topic labels from the retrieved radiology diagnostic reports. We then fuse the dynamic disease topic labels with the original visual features of the images to highlight the abnormal regions in the original visual features to alleviate the visual data bias problem. The SKE module expands upon the conventional static knowledge graph to mitigate textual data biases and amplify the interpretability capabilities of the model via domain-specific dynamic knowledge graphs. The MKI distills all the knowledge and generates the final diagnostic radiology report. We performed extensive experiments on two widely used datasets, IU X-Ray and MIMIC-CXR. The experimental results demonstrate the effectiveness of our method, with all evaluation metrics outperforming previous state-of-the-art models.