README.md

---
license: cc-by-nc-4.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
  - split: val
    path: data/val-*
- config_name: original
  data_files:
  - split: train
    path: original/train-*
  - split: test
    path: original/test-*
  - split: val
    path: original/val-*
dataset_info:
- config_name: default
  features:
  - name: UID
    dtype: string
  - name: Fold
    dtype: int64
  - name: Split
    dtype: string
  - name: PatientID
    dtype: string
  - name: PhysicianID
    dtype: string
  - name: StudyDate
    dtype: string
  - name: Age
    dtype: int64
  - name: Sex
    dtype: string
  - name: HeartSize
    dtype: int64
  - name: PulmonaryCongestion
    dtype: int64
  - name: PleuralEffusion_Right
    dtype: int64
  - name: PleuralEffusion_Left
    dtype: int64
  - name: PulmonaryOpacities_Right
    dtype: int64
  - name: PulmonaryOpacities_Left
    dtype: int64
  - name: Atelectasis_Right
    dtype: int64
  - name: Atelectasis_Left
    dtype: int64
  - name: Image
    dtype: image
  splits:
  - name: train
    num_bytes: 36725048989.54
    num_examples: 137595
  - name: test
    num_bytes: 11088307165.008
    num_examples: 42928
  - name: val
    num_bytes: 9210720811.1
    num_examples: 34862
  download_size: 58345259132
  dataset_size: 57024076965.648
- config_name: original
  features:
  - name: UID
    dtype: string
  - name: Fold
    dtype: int64
  - name: Split
    dtype: string
  - name: PatientID
    dtype: string
  - name: PhysicianID
    dtype: string
  - name: StudyDate
    dtype: string
  - name: Age
    dtype: int64
  - name: Sex
    dtype: string
  - name: HeartSize
    dtype: int64
  - name: PulmonaryCongestion
    dtype: int64
  - name: PleuralEffusion_Right
    dtype: int64
  - name: PleuralEffusion_Left
    dtype: int64
  - name: PulmonaryOpacities_Right
    dtype: int64
  - name: PulmonaryOpacities_Left
    dtype: int64
  - name: Atelectasis_Right
    dtype: int64
  - name: Atelectasis_Left
    dtype: int64
  - name: Image
    dtype: image
  splits:
  - name: train
    num_bytes: 793586998398.28
    num_examples: 137595
  - name: test
    num_bytes: 235100370576.352
    num_examples: 42928
  - name: val
    num_bytes: 197771374689.288
    num_examples: 34862
  download_size: 1266934126006
  dataset_size: 1226458743663.9202
extra_gated_prompt: >-
  ### 🛡️ Data Usage Agreement 


  By accessing and using the dataset, you agree to the following terms and
  conditions:


  1. **Purpose of Use**  
     This dataset is provided **solely for research and educational purposes**. Any commercial use is strictly prohibited without explicit written permission from the dataset creators.

  2. **Ethical Use**  
     You agree to use this dataset in an ethical manner, respecting human dignity, privacy, and all applicable laws and regulations. The data **must not be used to attempt to identify individuals** or for any discriminatory or harmful purposes.

  3. **Data Privacy**  
     This dataset may contain sensitive medical information. Although all personally identifiable information (PII) has been removed or anonymized to the best extent possible, you acknowledge your responsibility in ensuring that data remains de-identified and is not re-identified.

  4. **Compliance with Regulations**  
     You agree to comply with all applicable data protection regulations such as **HIPAA**, **GDPR**, or local equivalents.

  5. **No Redistribution**  
     You shall not share, redistribute, or publish the dataset in full or in part without explicit consent from the dataset authors.

  6. **Attribution**  
     Any published work or presentation using this dataset must **cite the original source** as specified in the dataset documentation.

  7. **Indemnity**  
     You agree to hold harmless and indemnify the dataset providers from and against any claims arising from your use of the dataset.

  8. **Revocation of Access**  
     The dataset creators reserve the right to revoke access to the dataset at any time, for any reason, including violations of this agreement.
task_categories:
- image-classification
language:
- en
tags:
- medical
- x-ray
- chest
- thorax
- radiograph
size_categories:
- 100K<n<1M
---

# 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. 
Please see our paper for a detailed description:  [Not yet available.](https://arxiv.org/abs/your-paper-link)

<br>

## 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 = 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 split to CSV files
df_split = metadata_df[["UID", "Split"]]
df_split.to_csv(metadata_dir / "split.csv", index=False) 

# 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!")
```