Datasets:
Update README.md
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README.md
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data_files:
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- split: test
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path: impression_section/test-*
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---
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data_files:
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- split: test
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path: impression_section/test-*
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task_categories:
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- image-text-to-text
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language:
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- en
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tags:
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- chest-xray
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- report-generation
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- mimic-cxr
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pretty_name: MIMIC-CXR Radiology Report Generation
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size_categories:
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- 1K<n<10K
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---
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# MIMIC-CXR-RRG: Radiology Report Generation Subsets
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This dataset provides two carefully filtered and structured subsets from the [MIMIC-CXR](https://physionet.org/content/mimic-cxr/2.0.0/) dataset, specifically designed for **Radiology Report Generation (RRG)** tasks. It includes image-report pairs focused on the **Findings** and **Impression** sections, targeting **frontal-view chest X-rays** only.
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---
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## ๐ Dataset Overview
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| Subset | Section Target | Split | #Samples | View Type |
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|---------------------|--------------------|-------|----------|----------------|
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| `findings_section` | Findings | test | 2361 | Frontal only |
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| `impression_section`| Impression | test | 2343 | Frontal only |
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- The splits **follow the evaluation protocol** used in models such as [Libra](https://x-izhang.github.io/Libra_v1.0/) and [MAIRA-2](https://arxiv.org/abs/2406.04449).
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- Images and labels are provided in a test-only setting, useful for **benchmarking and zero-shot evaluation**.
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---
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## ๐งพ Data Format
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Each instance in both subsets contains:
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- ๐ท `main_image` โ The frontal-view chest X-ray
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- ๐ท `prior_image` โ (Optional) Prior image if available
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- ๐ Text sections:
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- `findings_section`
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- `impression_section`
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- `indication_section`
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- `comparison_section`
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- `technique_section`
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- `history_section`
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- `examination_section`
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- ๐ฌ `default_prompt` โ Prompt for generation tasks
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- ๐งพ Metadata:
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- `dicom_id`, `study_id`, `subject_id`
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- Acquisition info: `Rows`, `Columns`, `StudyDate`, `ViewPosition`, etc.
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---
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## ๐ How to Use
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```python
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from datasets import load_dataset
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# Load a specific subset (e.g., findings_section)
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ds = load_dataset("X-iZhang/MIMIC-CXR-RRG", name="findings_section", split="test")
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# Display an image
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from PIL import Image
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ds[0]["main_image"].show()
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# View sample
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print(ds[0]["findings_section"])
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```
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## โ๏ธ Citation
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```
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@misc{zhang2025libraleveragingtemporalimages,
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title={Libra: Leveraging Temporal Images for Biomedical Radiology Analysis},
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author={Xi Zhang and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
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year={2025},
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eprint={2411.19378},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2411.19378},
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}
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```
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