size_categories:1K<n<10K language:en license:cc-by-nd-4.0 region:us
---
dataset_info:
  features:
  - name: uid
    dtype: int64
  - name: MeSH
    dtype: string
  - name: Problems
    dtype: string
  - name: image
    dtype: string
  - name: indication
    dtype: string
  - name: comparison
    dtype: string
  - name: findings
    dtype: string
  - name: impression
    dtype: string
  - name: img_frontal
    dtype: binary
  - name: img_lateral
    dtype: binary
  splits:
  - name: train
    num_bytes: 2104109741
    num_examples: 3851
  download_size: 2095869611
  dataset_size: 2104109741
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
license: cc-by-nd-4.0
language:
- en
pretty_name: Chest X-rays (Indiana University)
size_categories:
- 1K<n<10K
---
# Chest X-rays (Indiana University)

Copy of the kaggle dataset: https://www.kaggle.com/datasets/raddar/chest-xrays-indiana-university created by [raddar](https://www.kaggle.com/raddar)

---
Open access chest X-ray collection from Indiana University

Original source: https://openi.nlm.nih.gov/

Original images were downloaded in raw DICOM standard. Each image was converted to png using some post-processing:

    top/bottom 0.5% DICOM pixel values were clipped (to eliminate very dark or very bright pixel outliers)
    DICOM pixel values scaled linearly to fit into 0-255 range
    resized to 2048 on shorter side (to fit in Kaggle dataset limits)

Metadata downloaded using available API (https://openi.nlm.nih.gov/services#searchAPIUsingGET)

Each image classified manually into frontal and lateral chest X-ray categories.

License: [Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) ](https://creativecommons.org/licenses/by-nc-nd/4.0/)

---

### Usage

The lateral and frontal images of each uid are grouped together in an example. The images are stored as bytes, and can be loaded to PIL Image using the following method

```
def load_image_from_byte_array(byte_array):
    return Image.open(io.BytesIO(byte_array))
```

### Cite

Please site the original [source of the dataset](https://openi.nlm.nih.gov/).

```
@article{demner2016preparing,
  title={Preparing a collection of radiology examinations for distribution and retrieval},
  author={Demner-Fushman, Dina and Kohli, Marc D and Rosenman, Marc B and Shooshan, Sonya E and Rodriguez, Laritza and Antani, Sameer and Thoma, George R and McDonald, Clement J},
  journal={Journal of the American Medical Informatics Association},
  volume={23},
  number={2},
  pages={304--310},
  year={2016},
  publisher={Oxford University Press}
}
```
