Update README.md

README.md

@@ -117,4 +117,83 @@ configs:
   data_files:
   - split: test
     path: impression_section/test-*
+task_categories:
+- image-text-to-text
+language:
+- en
+tags:
+- chest-xray
+- report-generation
+- mimic-cxr
+pretty_name: MIMIC-CXR Radiology Report Generation
+size_categories:
+- 1K<n<10K
 ---
+
+# MIMIC-CXR-RRG: Radiology Report Generation Subsets
+
+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.
+
+---
+
+## 📚 Dataset Overview
+
+| Subset              | Section Target     | Split | #Samples | View Type     |
+|---------------------|--------------------|-------|----------|----------------|
+| `findings_section`  | Findings            | test  | 2361     | Frontal only   |
+| `impression_section`| Impression          | test  | 2343     | Frontal only   |
+
+- 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).
+- Images and labels are provided in a test-only setting, useful for **benchmarking and zero-shot evaluation**.
+
+---
+
+## 🧾 Data Format
+
+Each instance in both subsets contains:
+
+- 📷 `main_image` – The frontal-view chest X-ray
+- 📷 `prior_image` – (Optional) Prior image if available
+- 📝 Text sections:
+  - `findings_section`
+  - `impression_section`
+  - `indication_section`
+  - `comparison_section`
+  - `technique_section`
+  - `history_section`
+  - `examination_section`
+- 💬 `default_prompt` – Prompt for generation tasks
+- 🧾 Metadata:
+  - `dicom_id`, `study_id`, `subject_id`
+  - Acquisition info: `Rows`, `Columns`, `StudyDate`, `ViewPosition`, etc.
+
+---
+
+## 🚀 How to Use
+
+```python
+from datasets import load_dataset
+
+# Load a specific subset (e.g., findings_section)
+ds = load_dataset("X-iZhang/MIMIC-CXR-RRG", name="findings_section", split="test")
+
+# Display an image
+from PIL import Image
+ds[0]["main_image"].show()
+
+# View sample
+print(ds[0]["findings_section"])
+```
+
+## ✏️ Citation
+```
+@misc{zhang2025libraleveragingtemporalimages,
+      title={Libra: Leveraging Temporal Images for Biomedical Radiology Analysis}, 
+      author={Xi Zhang and Zaiqiao Meng and Jake Lever and Edmond S. L. Ho},
+      year={2025},
+      eprint={2411.19378},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV},
+      url={https://arxiv.org/abs/2411.19378}, 
+}
+```