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.

• 2025-03-16 Release checkpoints for MIMIC-ABN and Two-view CXR.
• 2025-03-01 Upload official code and checkpoints for MIMIC-CXR.
• 2025-03-01 Release generated-radiology-reports, the labels column corresponds to reference reports, while the report column represents generated reports.
• 2025-02-28 Release .

• torch==2.3.1+cu118
• transformers==4.43.3
• torchvision==0.18.1+cu118
• radgraph==0.09
• python==3.9.0
• please refer to requirements.txt for more details.
• As stated in Issue #4, the transformers version should be maintained to prevent potential problems. Credit goes to @Andy for this clarification.
• Set up the same virtual environment as ours：

% create virtual environment
conda create -n mlrg python=3.9.0
% install packages
pip install -r requirements.txt

• Checkpoints (pretrain and finetune) and logs for the MIMIC-CXR dataset are available at Baidu Netdisk and huggingface 🤗.
• MIMIC-ABN
• Two-view CXR

• MIMIC-CXR and MIMIC-ABN are publicly accessible through PhysioNet, with data systematically organized under root directories labeled p10 through p19, maintaining consistency with MIMIC-CXR's default configuration.
• IU X-ray dataset is publicly available at NIH, and its root directory is the NLMCXR_png.
• Two-View CXR dataset: the NLMCXR_png + MIMIC-CXR images. Two-view CXR aggregates studies with two views from MIMIC-CXR and IU X-ray. For more details, please refer to arXiv.
• The comprehensive file architecture for all datasets is structured as delineated below:

files/
├── p10
    └── p10000032
            └── s50414267
               ├── 02aa804e-bde0afdd-112c0b34-7bc16630-4e384014.jpg
               └── 174413ec-4ec4c1f7-34ea26b7-c5f994f8-79ef1962.jpg
├── p11
├── p12
├── p13
├── p14
├── p15
├── p16
├── p17
├── p18
├── p19
└── NLMCXR_png
   ├── CXR1_1_IM-0001-3001.png
   ├── CXR1_1_IM-0001-4001.png
   └── CXR2_IM-0652-1001.png

• MIMIC-CXR and MIMIC-ABN: PhysioNet.
• Two-view CXR: huggingface 🤗.

• To simplify usage, we have organized multi-view longitudinal data using the study_id. The processed datasets—MIMIC-CXR, MIMIC-ABN, and Two-view CXR—are available on Hugging Face 🤗 (PhysioNet authorization required). Note that the IU X-ray dataset (NLMCXR_png) does not include previous visit data due to the absence of study_id.
• MIMIC-CXR: five_work_mimic_cxr_annotation_v1.1.json
• MIMIC-ABN: mlrg_mimic_abn_annotation_v1.1.json
• Two-view CXR: mlrg_multiview_cxr_annotation_v1.1.json
• View Position for all datasets: five_work_mimic_cxr_view_position_v1.1.json

def compute_performance_using_generated_reports():
    from tools.metrics.metrics import compute_all_scores, compute_chexbert_details_scores
    mimic_cxr_generated_path = 'generated-radiology-reports/MIMIC-CXR/test_reports_epoch-1_20-10-2024_16-28-28.csv'
    mimic_abn_generated_path = 'generated-radiology-reports/MIMIC-ABN/test_reports_epoch-1_23-10-2024_10-25-20.csv'
    twoview_cxr_generated_path = 'generated-radiology-reports/Two-view CXR/test_reports_epoch-0_25-10-2024_11-38-35.csv'
    args = {
        'chexbert_path': "/home/miao/data/dataset/checkpoints/chexbert.pth",
        'bert_path': "/home/miao/data/dataset/checkpoints/bert-base-uncased",
        'radgraph_path': "/home/miao/data/dataset/checkpoints/radgraph",
    }
    for generated_path in [mimic_cxr_generated_path, mimic_abn_generated_path, twoview_cxr_generated_path]:
        data = pd.read_csv(generated_path)
        gts, gens = data['labels'].tolist(), data['report'].tolist()
        scores = compute_all_scores(gts, gens, args)
        print(scores)

1. Download checkpoints for evaluation or initialization.

• For CE metrics calculation: chexbert.pth, radgraph, and bert-base-uncased.
• For model initialization: microsoft/rad-dino (image encoder), microsoft/BiomedVLP-CXR-BERT-specialized (text encoder), distilbert/distilgpt2 (define text generator), and cvt2distilgpt2 (initialize text generator).
• Checkpoint directory: Place all checkpoints in a local directory (e.g., "/home/data/checkpoints"), and configure the --ckpt_zoo_dir /home/data/checkpoints argument in the corresponding script/**/**.sh file.

2. Conducting Stages 1 and 2

# Stage 1: Multi-view Longitudinal Contrastive Learning
cd script/MIMIC-CXR
bash run_cxr_pt_v0906_fs.sh
# Stage 2: Chest X-ray Report Generation based on Patient-specific Prior Knowledge
cd script/MIMIC-CXR
bash run_cxr_ft_mlrg_v1011.sh

If you use or extend our work, please cite our paper at CVPR 2025.

@misc{liu2025-mlrg,
      title={Enhanced Contrastive Learning with Multi-view Longitudinal Data for Chest X-ray Report Generation}, 
      author={Kang Liu and Zhuoqi Ma and Xiaolu Kang and Yunan Li and Kun Xie and Zhicheng Jiao and Qiguang Miao},
      year={2025},
      eprint={2502.20056},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2502.20056}, 
}

• cvt2distilgpt2 Some codes are adapted based on R2Gen.
• EVOKE Some code implementations are adapted from EVOKE, and the Two-view CXR dataset is sourced from this work.

[1] Nicolson, A., Dowling, J., & Koopman, B. (2023). Improving chest X-ray report generation by leveraging warm starting. Artificial Intelligence in Medicine, 144, 102633.

[2] Liu, K., Ma, Z., Xie, K., Jiao, Z., & Miao, Q. (2024). MCL: Multi-view Enhanced Contrastive Learning for Chest X-ray Report Generation. arXiv preprint arXiv:2411.10224.