🩺 CoGaze: Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays

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✨ Overview

CoGaze is a vision-language pretraining framework designed for chest X-ray understanding, inspired by how radiologists interpret medical images.

It integrates:

• 👁️ Gaze information is used during pretraining, while downstream tasks (report generation, classification, and segmentation) do not require gaze data.
• 🧠 Context-aware reasoning
• 📝 Free-text & structured report generation, supervised & zero-shot classification, segmentation, image-text retrieval

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📰 News

• [2026-03-28] 🚀 Official code and pretrained models are released on Hugging Face

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⚙️ Installation

# Create conda environment
conda create -n cogaze python=3.10.16
conda activate cogaze

📦 Core Dependencies

transformers==4.43.3
radgraph==0.0.9
pytorch-lighting==2.5.1.post0
torch==2.4.1
torchvision==0.19.1

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🧩 Model Zoo

Dataset	Pretrained Model	Report Generation Model	Outputs
MIMIC-CXR	CoGaze Pretrained Checkpoint	CoGaze (DistilGPT2)	Generated Reports

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📁 Dataset Preparation

1️⃣ MIMIC-CXR Images

Dataset source: PhysioNet

data/
├── p10/
│   └── p10000032/
│       └── s50414267/
│           ├── image1.jpg
│           └── image2.jpg
├── p11/
└── ...

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2️⃣ Annotations & Reports

Available on 🤗 Hugging Face:

• Gaze heatmap
• Image-text pairs
• SRRG annotations

👉 https://huggingface.co/MK-runner/CoGaze/tree/main/mimic-annotation

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3️⃣ Checkpoint Structure

ckpt_zoo_dir/
├── chexbert.pth
├── radgraph/
├── google-bert/
├── microsoft/
└── distilgpt2/

⚠️ Manual download required:

• chexbert.pth
• radgraph

See: https://github.com/mk-runner/MLRG

💡 Tip: Enable automatic download during training:

--online_ckpt "Yes"

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4️⃣ Additional Datasets

Task	Dataset
Classification	NIH Chest X-rays
Detection	RSNA Pneumonia
Segmentation	SIIM-ACR
Tuberculosis	TBX11K
External	Shenzhen Dataset

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🧠 Training & Inference

🔹 Pretraining

bash script/pretrain.sh

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🔹 Report Generation

Free-text (Training)

bash script/free-text-report-generation-gpt2.sh
bash script/free-text-report-generation-llm.sh

Free-text (Inference)

bash script/free-text-report-generation-gpt2-inference.sh

Structured Reports

bash script/structured-report-generation-gpt2.sh

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📊 Evaluation

🔹 Compute Metrics

from tools.metrics.metrics import compute_all_scores
import pandas as pd

data = pd.read_csv("generated_reports/xxx.csv")
gts = data['reference_report'].tolist()
gens = data['generated_report'].tolist()

scores = compute_all_scores(gts, gens, args)
print(scores)

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📈 Performance (DistilGPT2)

{
    'BertScore': 0.5956377387046814,
    'Radgraph-simple': 0.30690433233898795,
    'Radgraph-partial': 0.28076371917819565,
    'Radgraph-complete': 0.22603009157065043,
    'SemScore': 0.45877182483673096,
    '1/RadCliQ-V1': 1.082196619824061,
    'RATEScore': 0.5787309255637078,
    'chexbert_5_micro_f1': 0.5708835341365461,
    'chexbert_5_macro_f1': 0.49498245207765257,
    'chexbert_all_micro_p': 0.5544458762886598,
    'chexbert_all_micro_r': 0.4980706154736639,
    'chexbert_all_micro_f1': 0.5247484500457363,
    'chexbert_all_macro_p': 0.44258976034375364,
    'chexbert_all_macro_r': 0.37672752858687886,
    'chexbert_all_macro_f1': 0.3883859770668801,
    'BLEU_1': 0.4103171077382396,
    'BLEU_2': 0.28970066408787387,
    'BLEU_3': 0.22010546378006685,
    'BLEU_4': 0.17481171574606008,
    'METEOR': 0.19054219748683743,
    'ROUGE_L': 0.3257898419599922,
    'CIDer': 0.3962696560568994
}

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📚 Citation

@misc{2026-cogaze,
      title={Seeing Like Radiologists: Context- and Gaze-Guided Vision-Language Pretraining for Chest X-rays}, 
      author={Kang Liu and Zhuoqi Ma and Siyu Liang and Yunan Li and Xiyue Gao and Chao Liang and Kun Xie and Qiguang Miao},
      year={2026},
      eprint={2603.26049},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2603.26049}, 
}

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🙏 Acknowledgements

• MLRG — dataset & evaluation tools
• cvt2distilgpt2 — text generation initialization

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⭐ Support

If you find this project useful:

• ⭐ Star this repository
• 🐛 Open issues for questions or bugs
• 📬 Contact Kang Liu (kangliu422@gmail.com) for collaboration

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