# Longitudinal Data and a Semantic Similarity Reward for Chest X-Ray Report Generation

arXiv: https://arxiv.org/abs/2307.09758 · HTML: https://mlsanity.com/paper/2307.09758/

## Key facts

- Cited by 24 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 56 references that are in the map.
- First posted on arXiv 19 Jul 2023.

## Builds on (most cited first, 30 of 31)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [CIDEr: Consensus-based Image Description Evaluation](https://mlsanity.com/paper/1411.5726/) (2014)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [LLaVA-Med: Training a Large Language-and-Vision Assistant for Biomedicine in One Day](https://mlsanity.com/paper/2306.00890/) (2023)
- [CvT: Introducing Convolutions to Vision Transformers](https://mlsanity.com/paper/2103.15808/) (2021)
- [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)
- [Making the Most of Text Semantics to Improve Biomedical Vision--Language Processing](https://mlsanity.com/paper/2204.09817/) (2022)
- [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)
- [Learning to Exploit Temporal Structure for Biomedical Vision-Language Processing](https://mlsanity.com/paper/2301.04558/) (2023)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [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)
- [ChatCAD: Interactive Computer-Aided Diagnosis on Medical Image using Large Language Models](https://mlsanity.com/paper/2302.07257/) (2023)
- [Neural Machine Translation with Byte-Level Subwords](https://mlsanity.com/paper/1909.03341/) (2019)
- [Improving Chest X-Ray Report Generation by Leveraging Warm Starting](https://mlsanity.com/paper/2201.09405/) (2022)
- [Improving the Factual Correctness of Radiology Report Generation with Semantic Rewards](https://mlsanity.com/paper/2210.12186/) (2022)
- [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)
- [LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation](https://mlsanity.com/paper/2305.11490/) (2023)
- [A Medical Semantic-Assisted Transformer for Radiographic Report Generation](https://mlsanity.com/paper/2208.10358/) (2022)
- [Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation](https://mlsanity.com/paper/2109.12242/) (2021)
- [Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports](https://mlsanity.com/paper/2306.08749/) (2023)
- [Variational Topic Inference for Chest X-Ray Report Generation](https://mlsanity.com/paper/2107.07314/) (2021)
- [A Self-Guided Framework for Radiology Report Generation](https://mlsanity.com/paper/2206.09378/) (2022)

## Built on by (most cited first, 24 of 24)

- [CXPMRG-Bench: Pre-training and Benchmarking for X-ray Medical Report Generation on CheXpert Plus Dataset](https://mlsanity.com/paper/2410.00379/) (2024)
- [LLM-RG4: Flexible and Factual Radiology Report Generation across Diverse Input Contexts](https://mlsanity.com/paper/2412.12001/) (2024)
- [e-Health CSIRO at RRG24: Entropy-Augmented Self-Critical Sequence Training for Radiology Report Generation](https://mlsanity.com/paper/2408.03500/) (2024)
- [DDaTR: Dynamic Difference-aware Temporal Residual Network for Longitudinal Radiology Report Generation](https://mlsanity.com/paper/2505.03401/) (2025)
- [CXRAgent: Director-Orchestrated Multi-Stage Reasoning for Chest X-Ray Interpretation](https://mlsanity.com/paper/2510.21324/) (2025)
- [XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation](https://mlsanity.com/paper/2501.04614/) (2025)
- [EVOKE: Elevating Chest X-ray Report Generation via Multi-View Contrastive Learning and Patient-Specific Knowledge](https://mlsanity.com/paper/2411.10224/) (2024)
- [MRG-R1: Reinforcement Learning for Clinically Aligned Medical Report Generation](https://mlsanity.com/paper/2512.16145/) (2025)
- [Natural Language Generation in Healthcare: A Review of Methods and Applications](https://mlsanity.com/paper/2505.04073/) (2025)
- [A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation](https://mlsanity.com/paper/2510.12444/) (2025)
- [Towards Predicting Temporal Changes in a Patient's Chest X-ray Images based on Electronic Health Records](https://mlsanity.com/paper/2409.07012/) (2024)
- [LaB-RAG: Label Boosted Retrieval Augmented Generation for Radiology Report Generation](https://mlsanity.com/paper/2411.16523/) (2024)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation](https://mlsanity.com/paper/2509.18600/) (2025)
- [Generalised Medical Phrase Grounding](https://mlsanity.com/paper/2512.01085/) (2025)
- [Advances and Challenges in Semantic Textual Similarity: A Comprehensive Survey](https://mlsanity.com/paper/2601.03270/) (2025)
- [Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation](https://mlsanity.com/paper/2603.16876/) (2026)
- [Not All Tokens Matter Equally: Dynamic In-context Vector Distillation with Decisive-Token Supervision for Long-form Medical Report Generation](https://mlsanity.com/paper/2605.27194/) (2026)
- [Privacy-Preserving Chest X-ray Report Generation via Multimodal Federated Learning with ViT and GPT-2](https://mlsanity.com/paper/2505.21715/) (2025)
- [Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation](https://mlsanity.com/paper/2601.16753/) (2026)
- [CXRMate-2: Structured Multimodal Temporal Embeddings and Tractable Reinforcement Learning for Clinically Acceptable Chest X-ray Radiology Report Generation](https://mlsanity.com/paper/2604.18967/) (2026)
- [Transition-Aware best-of-N sampling for Longitudinal Chest X-ray Reports](https://mlsanity.com/paper/2606.28393/) (2026)
- [PDD-RRG: Posterior Diagnostic Decision for Study-level Radiology Report Generation](https://mlsanity.com/paper/2608.03055/) (2026)
- [BrainDiff: Longitudinal Report Generation for Multimodal Brain MRI](https://mlsanity.com/paper/2609.00593/) (2026)

## Abstract (arXiv)

Radiologists face high burnout rates, partially due to the increasing volume of Chest X-rays (CXRs) requiring interpretation and reporting. Automated CXR report generation holds promise for reducing this burden and improving patient care. While current models show potential, their diagnostic accuracy is limited. Our proposed CXR report generator integrates elements of the radiologist workflow and introduces a novel reward for reinforcement learning. Our approach leverages longitudinal data from a patient's prior CXR study and effectively handles cases where no prior study exist, thus mirroring the radiologist's workflow. In contrast, existing models typically lack this flexibility, often requiring prior studies for the model to function optimally. Our approach also incorporates all CXRs from a patient's study and distinguishes between report sections through section embeddings. Our reward for reinforcement learning leverages CXR-BERT, which forces our model to learn the clinical semantics of radiology reporting. We conduct experiments on publicly available datasets -- MIMIC-CXR and Open-i IU X-ray -- with metrics shown to more closely correlate with radiologists' assessment of reporting. Results from our study demonstrate that the proposed model generates reports that are more aligned with radiologists' reports than state-of-the-art models, such as those utilising large language models, reinforcement learning, and multi-task learning. The proposed model improves the diagnostic accuracy of CXR report generation, which could one day reduce radiologists' workload and enhance patient care. Our Hugging Face checkpoint (https://huggingface.co/aehrc/cxrmate) and code (https://github.com/aehrc/cxrmate) are publicly available.