# Improving Chest X-Ray Report Generation by Leveraging Warm Starting

arXiv: https://arxiv.org/abs/2201.09405 · HTML: https://mlsanity.com/paper/2201.09405/

## Key facts

- Cited by 95 papers in the map (data through 14 Oct 2026).
- Builds on 45 of its 84 references that are in the map.
- First posted on arXiv 24 Jan 2022.

## Builds on (most cited first, 30 of 45)

- [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)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [ImageNet Large Scale Visual Recognition Challenge](https://mlsanity.com/paper/1409.0575/) (2014)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension](https://mlsanity.com/paper/1910.13461/) (2019)
- [Training data-efficient image transformers & distillation through attention](https://mlsanity.com/paper/2012.12877/) (2020)
- [DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter](https://mlsanity.com/paper/1910.01108/) (2019)
- [BEiT: BERT Pre-Training of Image Transformers](https://mlsanity.com/paper/2106.08254/) (2021)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [BioBERT: a pre-trained biomedical language representation model for biomedical text mining](https://mlsanity.com/paper/1901.08746/) (2019)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [SciBERT: A Pretrained Language Model for Scientific Text](https://mlsanity.com/paper/1903.10676/) (2019)
- [A Comprehensive Survey on Transfer Learning](https://mlsanity.com/paper/1911.02685/) (2019)
- [Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Books](https://mlsanity.com/paper/1506.06724/) (2015)
- [Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing](https://mlsanity.com/paper/2007.15779/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [Big Transfer (BiT): General Visual Representation Learning](https://mlsanity.com/paper/1912.11370/) (2019)
- [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)
- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Intriguing Properties of Vision Transformers](https://mlsanity.com/paper/2105.10497/) (2021)
- [Early Convolutions Help Transformers See Better](https://mlsanity.com/paper/2106.14881/) (2021)
- [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)
- [XCiT: Cross-Covariance Image Transformers](https://mlsanity.com/paper/2106.09681/) (2021)
- [CheXbert: Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERT](https://mlsanity.com/paper/2004.09167/) (2020)

## Built on by (most cited first, 30 of 95)

- [Towards Generalist Biomedical AI](https://mlsanity.com/paper/2307.14334/) (2023)
- [Conformal Language Modeling](https://mlsanity.com/paper/2306.10193/) (2023)
- [A Survey of Large Language Models for Healthcare: from Data, Technology, and Applications to Accountability and Ethics](https://mlsanity.com/paper/2310.05694/) (2023)
- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models](https://mlsanity.com/paper/2302.07257/) (2023)
- [PromptMRG: Diagnosis-Driven Prompts for Medical Report Generation](https://mlsanity.com/paper/2308.12604/) (2023)
- [MAIRA-1: A specialised large multimodal model for radiology report generation](https://mlsanity.com/paper/2311.13668/) (2023)
- [A Comprehensive Survey of Foundation Models in Medicine](https://mlsanity.com/paper/2406.10729/) (2024)
- [MedImageInsight: An Open-Source Embedding Model for General Domain Medical Imaging](https://mlsanity.com/paper/2410.06542/) (2024)
- [HistGen: Histopathology Report Generation via Local-Global Feature Encoding and Cross-modal Context Interaction](https://mlsanity.com/paper/2403.05396/) (2024)
- [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)
- [Multimodal Image-Text Matching Improves Retrieval-based Chest X-Ray Report Generation](https://mlsanity.com/paper/2303.17579/) (2023)
- [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)
- [Dia-LLaMA: Towards Large Language Model-driven CT Report Generation](https://mlsanity.com/paper/2403.16386/) (2024)
- [Towards a Holistic Framework for Multimodal Large Language Models in Three-dimensional Brain CT Report Generation](https://mlsanity.com/paper/2407.02235/) (2024)
- [Large Language Model with Region-guided Referring and Grounding for CT Report Generation](https://mlsanity.com/paper/2411.15539/) (2024)
- [An X-Ray Is Worth 15 Features: Sparse Autoencoders for Interpretable Radiology Report Generation](https://mlsanity.com/paper/2410.03334/) (2024)
- [ReXrank: A Public Leaderboard for AI-Powered Radiology Report Generation](https://mlsanity.com/paper/2411.15122/) (2024)
- [Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation](https://mlsanity.com/paper/2307.09758/) (2023)
- [ChEX: Interactive Localization and Region Description in Chest X-rays](https://mlsanity.com/paper/2404.15770/) (2024)
- [FineRadScore: A Radiology Report Line-by-Line Evaluation Technique Generating Corrections with Severity Scores](https://mlsanity.com/paper/2405.20613/) (2024)
- [A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry](https://mlsanity.com/paper/2404.15777/) (2024)
- [M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation](https://mlsanity.com/paper/2408.16213/) (2024)
- [Structural Entities Extraction and Patient Indications Incorporation for Chest X-ray Report Generation](https://mlsanity.com/paper/2405.14905/) (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)
- [Consensus, dissensus and synergy between clinicians and specialist foundation models in radiology report generation](https://mlsanity.com/paper/2311.18260/) (2023)
- [R2GenCSR: Mining Contextual and Residual Information for LLMs-based Radiology Report Generation](https://mlsanity.com/paper/2408.09743/) (2024)

## Abstract (arXiv)

Automatically generating a report from a patient's Chest X-Rays (CXRs) is a promising solution to reducing clinical workload and improving patient care. However, current CXR report generators -- which are predominantly encoder-to-decoder models -- lack the diagnostic accuracy to be deployed in a clinical setting. To improve CXR report generation, we investigate warm starting the encoder and decoder with recent open-source computer vision and natural language processing checkpoints, such as the Vision Transformer (ViT) and PubMedBERT. To this end, each checkpoint is evaluated on the MIMIC-CXR and IU X-Ray datasets. Our experimental investigation demonstrates that the Convolutional vision Transformer (CvT) ImageNet-21K and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoints are best for warm starting the encoder and decoder, respectively. Compared to the state-of-the-art (M2 Transformer Progressive), CvT2DistilGPT2 attained an improvement of 8.3% for CE F-1, 1.8% for BLEU-4, 1.6% for ROUGE-L, and 1.0% for METEOR. The reports generated by CvT2DistilGPT2 have a higher similarity to radiologist reports than previous approaches. This indicates that leveraging warm starting improves CXR report generation. Code and checkpoints for CvT2DistilGPT2 are available at https://github.com/aehrc/cvt2distilgpt2.