Update README.md

README.md

@@ -26,4 +26,104 @@ configs:
   data_files:
   - split: train
     path: data/train-*
+license: cc-by-nc-sa-4.0
+task_categories:
+- image-classification
+language:
+- en
+tags:
+- mri
+- medical
+- cardiac
+- imaging
+pretty_name: Automated Cardiac Diagnosis Challenge
+size_categories:
+- 10K<n<100K
 ---
+
+# Automated Cardiac Diagnosis Challenge (ACDC)
+
+This dataset contains materials from the *Automated Cardiac Diagnosis Challenge (ACDC)* introduced during MICCAI 2017 by Bernard et al., designed to advance research in **cardiac MRI analysis, representation learning, and automated cardiac disease understanding**.
+
+The dataset includes cine cardiac MRI acquisitions from healthy subjects and patients with multiple cardiac pathologies.
+
+
+## 🫀 Dataset Description
+
+This repository provides a **2D slice-based version** of the original ACDC cardiac MRI dataset, designed for efficient deep learning and representation learning workflows.
+
+Each entry corresponds to a **single 2D cardiac MRI slice** extracted from a cine cardiac MRI sequence.
+
+
+## 📦 Dataset Structure
+
+Each dataset entry contains:
+
+- `volume_id` → Unique identifier for the MRI volume/patient  
+- `time_id` → Temporal frame index within the cardiac cycle  
+- `slice_id` → Slice index within the volume  
+- `image` → 2D cardiac MRI slice  
+- `width` → Image width  
+- `height` → Image height  
+
+## Split
+The original dataset marks patients from 1 to 100 as training and 101 to 150 as testing.
+In this dataset the train split contains all the patients and it is for the user to decide the split.
+
+## ❤️ Clinical Categories
+
+The original ACDC dataset includes subjects from the following groups:
+
+- Healthy controls (NOR) (20 Training + 10 Testing)
+- Myocardial infarction (MINF) (20 Training + 10 Testing)
+- Dilated cardiomyopathy (DCM) (20 Training + 10 Testing)
+- Hypertrophic cardiomyopathy (HCM) (20 Training + 10 Testing)
+- Abnormal right ventricle (RV) (20 Training + 10 Testing)
+
+
+## Use
+
+```python
+from datasets import load_dataset
+import matplotlib.pyplot as plt
+
+dataset = load_dataset("chehablaborg/acdc_2d", split="train")
+
+sample_id = 314
+
+image = dataset[sample_id]["image"]
+time_id = dataset[sample_id]["time_id"]
+slice_id = dataset[sample_id]["slice_id"]
+
+plt.imshow(image, cmap="gray")
+plt.title(f"time={time_id}, slice={slice_id}")
+plt.axis("off")
+plt.show()
+```
+
+
+## 📚 Citation
+
+If you use this dataset, please mention us https://chehablab.com in an acknowledgement and cite the original publication:
+
+```bibtex
+@article{bernard2018deep,
+  title={Deep Learning Techniques for Automatic MRI Cardiac Multi-structures Segmentation and Diagnosis: Is the Problem Solved?},
+  author={Bernard, Olivier and Lalande, Alain and Zotti, Cl{\'e}ment and Cervenansky, Fr{\'e}d{\'e}ric and others},
+  journal={IEEE Transactions on Medical Imaging},
+  volume={37},
+  number={11},
+  pages={2514--2525},
+  year={2018},
+  month={nov},
+  doi={10.1109/TMI.2018.2837502}
+}
+```
+
+
+### License
+This work is licensed under a [Creative Commons CC BY-NC-SA 4.0 License](https://creativecommons.org/licenses/by-nc-sa/4.0/).
+[![CC BY-NC-SA 4.0](https://licensebuttons.net/l/by-nc-sa/4.0/88x31.png)](https://creativecommons.org/licenses/by-nc-sa/4.0/)
+
+
+[Chehab Lab](https://chehablab.com) @ 2026