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README.md

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 ---
+
 license: cc-by-4.0
 task_categories:
 - image-classification
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   download_size: 1266898242525
   dataset_size: 1226435862593.624
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+
+# TAIX-Ray Dataset
+
+TAIX-Ray is a comprehensive dataset of about 200k bedside chest radiographs from about 50k intensive care patients at the University Hospital in Aachen, Germany, collected between 2010 and 2024. 
+Trained radiologists provided structured reports at the time of acquisition, assessing key findings such as cardiomegaly, pulmonary congestion, pleural effusion, pulmonary opacities, and atelectasis on an ordinal scale. 
+
+
+<br>
+
+## Code & Details:
+The code for data loading, preprocessing, and baseline experiments is available at:  https://github.com/mueller-franzes/TAIX-Ray
+
+## How to Use
+
+### Prerequisites
+Ensure you have the following dependencies installed:
+
+```bash
+pip install datasets matplotlib huggingface_hub pandas tqdm
+```
+
+### Configurations
+This dataset is available in two configurations. 
+
+| **Name** | **Size** | **Image Size** |
+|------------|----------|----------------|
+| default    | 62GB     | 512px          |
+| original   | 1.2TB    | variable       |
+
+
+### Option A: Use within the Hugging Face Framework
+If you want to use the dataset directly within the Hugging Face `datasets` library, you can load and visualize it as follows:
+
+```python
+from datasets import load_dataset
+from matplotlib import pyplot as plt
+
+# Load the TAIX-Ray dataset
+dataset = load_dataset("TLAIM/TAIX-Ray", name="default")
+
+# Access the training split (Fold 0)
+ds_train = dataset['train']
+
+# Retrieve a single sample from the training set
+item = ds_train[0]
+
+# Extract and display the image
+image = item['Image']
+plt.imshow(image, cmap='gray')
+plt.savefig('image.png')  # Save the image to a file
+plt.show()  # Display the image
+
+# Print metadata (excluding the image itself)
+for key in item.keys():
+    if key != 'Image':
+        print(f"{key}: {item[key]}")
+```
+
+### Option B: Downloading the Dataset 
+
+If you prefer to download the dataset to a specific folder, use the following script. This will create the following folder structure:
+```
+.
+├── data/
+│   ├── 549a816ae020fb7da68a31d7d62d73c418a069c77294fc084dd9f7bd717becb9.png
+│   ├── d8546c6108aad271211da996eb7e9eeabaf44d39cf0226a4301c3cbe12d84151.png
+│   └── ...
+└── metadata/
+    ├── annoation.csv
+    └── split.csv 
+```
+
+```python
+from datasets import load_dataset
+from huggingface_hub import hf_hub_download
+from pathlib import Path
+import pandas as pd
+from tqdm import tqdm
+
+# Define output paths
+output_root = Path("./TAIX-Ray")
+
+# Create folders 
+data_dir = output_root / "data"
+metadata_dir = output_root / "metadata"
+data_dir.mkdir(parents=True, exist_ok=True)
+metadata_dir.mkdir(parents=True, exist_ok=True)
+
+# Load dataset in streaming mode
+dataset = dataset = load_dataset("TLAIM/TAIX-Ray", name="default",  streaming=True)
+
+# Process dataset
+metadata = []
+for split, split_dataset in dataset.items():
+    print("-------- Start Download: ", split, " --------")
+    for item in tqdm(split_dataset, desc="Downloading"):  # Stream data one-by-one
+        uid = item["UID"]
+        img = item.pop("Image")  # PIL Image object
+
+        # Save image
+        img.save(data_dir / f"{uid}.png", format="PNG")
+
+        # Store metadata
+        metadata.append(item)  
+
+# Convert metadata to DataFrame
+metadata_df = pd.DataFrame(metadata)
+
+# Save annotations to CSV files
+metadata_df.drop(columns=["Split", "Fold"]).to_csv(metadata_dir / "annotation.csv", index=False)
+
+
+print("Dataset streamed and saved successfully!")
+```
+
+