# Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation

arXiv: https://arxiv.org/abs/2502.20056 · HTML: https://mlsanity.com/paper/2502.20056/

## Key facts

- Cited by 42 papers in the map (data through 14 Oct 2026).
- Builds on 47 of its 66 references that are in the map.
- First posted on arXiv 27 Feb 2025.

## Builds on (most cited first, 30 of 47)

- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [DINOv2: Learning Robust Visual Features without Supervision](https://mlsanity.com/paper/2304.07193/) (2023)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison](https://mlsanity.com/paper/1901.07031/) (2019)
- [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)
- [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)
- [StableRep: Synthetic Images from Text-to-Image Models Make Strong Visual Representation Learners](https://mlsanity.com/paper/2306.00984/) (2023)
- [Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning](https://mlsanity.com/paper/2210.06044/) (2022)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology](https://mlsanity.com/paper/2301.02228/) (2023)
- [When Radiology Report Generation Meets Knowledge Graph](https://mlsanity.com/paper/2002.08277/) (2020)
- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images](https://mlsanity.com/paper/2302.14042/) (2023)
- [GREEN: Generative Radiology Report Evaluation and Error Notation](https://mlsanity.com/paper/2405.03595/) (2024)
- [Dynamic Graph Enhanced Contrastive Learning for Chest X-ray Report Generation](https://mlsanity.com/paper/2303.10323/) (2023)
- [R2GenGPT: Radiology Report Generation with Frozen LLMs](https://mlsanity.com/paper/2309.09812/) (2023)
- [METransformer: Radiology Report Generation by Transformer with Multiple Learnable Expert Tokens](https://mlsanity.com/paper/2304.02211/) (2023)
- [CT2Rep: Automated Radiology Report Generation for 3D Medical Imaging](https://mlsanity.com/paper/2403.06801/) (2024)
- [Improving Chest X-Ray Report Generation by Leveraging Warm Starting](https://mlsanity.com/paper/2201.09405/) (2022)
- [Radiology Report Generation with a Learned Knowledge Base and Multi-modal Alignment](https://mlsanity.com/paper/2112.15011/) (2021)
- [Knowledge Matters: Radiology Report Generation with General and Specific Knowledge](https://mlsanity.com/paper/2112.15009/) (2021)
- [KiUT: Knowledge-injected U-Transformer for Radiology Report Generation](https://mlsanity.com/paper/2306.11345/) (2023)
- [LLM-CXR: Instruction-Finetuned LLM for CXR Image Understanding and Generation](https://mlsanity.com/paper/2305.11490/) (2023)
- [EHRXQA: A Multi-Modal Question Answering Dataset for Electronic Health Records with Chest X-ray Images](https://mlsanity.com/paper/2310.18652/) (2023)

## Built on by (most cited first, 30 of 42)

- [PriorRG: Prior-Guided Contrastive Pre-training and Coarse-to-Fine Decoding for Chest X-ray Report Generation](https://mlsanity.com/paper/2508.05353/) (2025)
- [Med-GLIP: Advancing Medical Language-Image Pre-training with Large-scale Grounded Dataset](https://mlsanity.com/paper/2508.10528/) (2025)
- [Structured Spectral Graph Representation Learning for Multi-label Abnormality Analysis from 3D CT Scans](https://mlsanity.com/paper/2510.10779/) (2025)
- [A Review of Longitudinal Radiology Report Generation: Dataset Composition, Methods, and Performance Evaluation](https://mlsanity.com/paper/2510.12444/) (2025)
- [Generative AI for Healthcare: Fundamentals, Challenges, and Perspectives](https://mlsanity.com/paper/2510.24551/) (2025)
- [Enhancing Reinforcement Learning for Radiology Report Generation with Evidence-aware Rewards and Self-correcting Preference Learning](https://mlsanity.com/paper/2604.13598/) (2026)
- [Generating Reports or Repeating Templates? Measuring and Mitigating Template Collapse in 3D CT Report Generation](https://mlsanity.com/paper/2605.30984/) (2026)
- [Automated Structured Radiology Report Generation with Rich Clinical Context](https://mlsanity.com/paper/2510.00428/) (2025)
- [TRACE: Temporal Radiology with Anatomical Change Explanation for Grounded X-ray Report Generation](https://mlsanity.com/paper/2602.02963/) (2026)
- [AnchorDiff: Topology-Aware Masked Diffusion with Confidence-based Rewriting for Radiology Report Generation](https://mlsanity.com/paper/2605.17071/) (2026)
- [CheXLearner: Text-Guided Fine-Grained Representation Learning for Progression Detection](https://mlsanity.com/paper/2505.06903/) (2025)
- [CLARIFID: Improving Radiology Report Generation by Reinforcing Clinically Accurate Impressions and Enforcing Detailed Findings](https://mlsanity.com/paper/2507.17234/) (2025)
- [OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation](https://mlsanity.com/paper/2509.18600/) (2025)
- [Discrete Diffusion Models with MLLMs for Unified Medical Multimodal Generation](https://mlsanity.com/paper/2510.06131/) (2025)
- [Multimodal Disease Progression Modeling via Spatiotemporal Disentanglement and Multiscale Alignment](https://mlsanity.com/paper/2510.11112/) (2025)
- [Multi-Modal Multi-Agent Reinforcement Learning for Radiology Report Generation](https://mlsanity.com/paper/2603.16876/) (2026)
- [Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays](https://mlsanity.com/paper/2603.26049/) (2026)
- [Temporal Inversion for Learning Interval Change in Chest X-Rays](https://mlsanity.com/paper/2604.04563/) (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)
- [Generative Sign-description Prompts with Multi-positive Contrastive Learning for Sign Language Recognition](https://mlsanity.com/paper/2505.02304/) (2025)
- [SURE-Med: Systematic Uncertainty Reduction for Enhanced Reliability in Medical Report Generation](https://mlsanity.com/paper/2508.01693/) (2025)
- [AMRG: Extend Vision Language Models for Automatic Mammography Report Generation](https://mlsanity.com/paper/2508.09225/) (2025)
- [Standardizing Longitudinal Radiology Report Evaluation via Large Language Model Annotation](https://mlsanity.com/paper/2601.16753/) (2026)
- [LEAD: Layer-wise Expert-aligned Decoding for Faithful Radiology Report Generation](https://mlsanity.com/paper/2602.04617/) (2026)
- [MMRad-22K: A Structured Multimodal Evidence Dataset for Chest X-ray Report Generation](https://mlsanity.com/paper/2602.12843/) (2026)
- [PRIMA: Pre-Training with Risk-Integrated Image--Metadata Alignment for Medical Diagnosis with LLM-Based Feature Aggregation](https://mlsanity.com/paper/2602.23297/) (2026)
- [Rethinking the Efficiency and Effectiveness of Reinforcement Learning for Radiology Report Generation](https://mlsanity.com/paper/2603.04022/) (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)
- [Precision Recall Controllable Radiology Report Generation via Hybrid Natural Language and Clinical Reward Learning](https://mlsanity.com/paper/2606.21447/) (2026)
- [Seeing Through Multiple Views: Parameter-Efficient Fine-Tuning via Selective Neurons for Consistent Radiology Report Generation](https://mlsanity.com/paper/2606.31099/) (2026)

## Abstract (arXiv)

Automated radiology report generation offers an effective solution to alleviate radiologists' workload. However, most existing methods focus primarily on single or fixed-view images to model current disease conditions, which limits diagnostic accuracy and overlooks disease progression. Although some approaches utilize longitudinal data to track disease progression, they still rely on single images to analyze current visits. To address these issues, we propose enhanced contrastive learning with Multi-view Longitudinal data to facilitate chest X-ray Report Generation, named MLRG. Specifically, we introduce a multi-view longitudinal contrastive learning method that integrates spatial information from current multi-view images and temporal information from longitudinal data. This method also utilizes the inherent spatiotemporal information of radiology reports to supervise the pre-training of visual and textual representations. Subsequently, we present a tokenized absence encoding technique to flexibly handle missing patient-specific prior knowledge, allowing the model to produce more accurate radiology reports based on available prior knowledge. Extensive experiments on MIMIC-CXR, MIMIC-ABN, and Two-view CXR datasets demonstrate that our MLRG outperforms recent state-of-the-art methods, achieving a 2.3% BLEU-4 improvement on MIMIC-CXR, a 5.5% F1 score improvement on MIMIC-ABN, and a 2.7% F1 RadGraph improvement on Two-view CXR.