# EIR: Enhanced Image Representations for Medical Report Generation

arXiv: https://arxiv.org/abs/2512.23185 · HTML: https://mlsanity.com/paper/2512.23185/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 51 references that are in the map.
- First posted on arXiv 29 Dec 2025.

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

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [Making the V in VQA Matter: Elevating the Role of Image Understanding in Visual Question Answering](https://mlsanity.com/paper/1612.00837/) (2016)
- [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)
- [Multi-view Convolutional Neural Networks for 3D Shape Recognition](https://mlsanity.com/paper/1505.00880/) (2015)
- [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)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [Attention on Attention for Image Captioning](https://mlsanity.com/paper/1908.06954/) (2019)
- [Knowing When to Look: Adaptive Attention via A Visual Sentinel for Image Captioning](https://mlsanity.com/paper/1612.01887/) (2016)
- [A Critical Review of Recurrent Neural Networks for Sequence Learning](https://mlsanity.com/paper/1506.00019/) (2015)
- [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)
- [Clinically Accurate Chest X-Ray Report Generation](https://mlsanity.com/paper/1904.02633/) (2019)
- [Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation](https://mlsanity.com/paper/2303.10323/) (2023)
- [A Hierarchical Approach for Generating Descriptive Image Paragraphs](https://mlsanity.com/paper/1611.06607/) (2016)
- [Knowledge-driven Encode, Retrieve, Paraphrase for Medical Image Report Generation](https://mlsanity.com/paper/1903.10122/) (2019)
- [Unsupervised Image Captioning](https://mlsanity.com/paper/1811.10787/) (2018)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [Comparing to Learn: Surpassing ImageNet Pretraining on Radiographs By Comparing Image Representations](https://mlsanity.com/paper/2007.07423/) (2020)
- [Deep Learning COVID-19 Features on CXR using Limited Training Data Sets](https://mlsanity.com/paper/2004.05758/) (2020)
- [Knowledge Matters: Radiology Report Generation with General and Specific Knowledge](https://mlsanity.com/paper/2112.15009/) (2021)
- [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports](https://mlsanity.com/paper/2004.12274/) (2020)

## Abstract (arXiv)

Generating medical reports from chest X-ray images is a critical and time-consuming task for radiologists, especially in emergencies. To alleviate the stress on radiologists and reduce the risk of misdiagnosis, numerous research efforts have been dedicated to automatic medical report generation in recent years. Most recent studies have developed methods that represent images by utilizing various medical metadata, such as the clinical document history of the current patient and the medical graphs constructed from retrieved reports of other similar patients. However, all existing methods integrate additional metadata representations with visual representations through a simple "Add and LayerNorm" operation, which suffers from the information asymmetry problem due to the distinct distributions between them. In addition, chest X-ray images are usually represented using pre-trained models based on natural domain images, which exhibit an obvious domain gap between general and medical domain images. To this end, we propose a novel approach called Enhanced Image Representations (EIR) for generating accurate chest X-ray reports. We utilize cross-modal transformers to fuse metadata representations with image representations, thereby effectively addressing the information asymmetry problem between them, and we leverage medical domain pre-trained models to encode medical images, effectively bridging the domain gap for image representation. Experimental results on the widely used MIMIC and Open-I datasets demonstrate the effectiveness of our proposed method.