# Indiana University Chest Xray Dataset Card

## Data sources:
This is a converted and processed version of the open access pneumonia chest x-ray dataset provided by the indiana university.<br>
You can see its information page [here](https://openi.nlm.nih.gov/faq).<br>
The compressed images in the png format were downloaded from [here](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_png.tgz) and the corresponding reports from [here](https://openi.nlm.nih.gov/imgs/collections/NLMCXR_reports.tgz).

## Data fields:
There are two fields: image and text.
The images are the x-rays and the texts are their associated findings.

## Preprocessing done:

1. **Make all text lowercase**: Convert all text to lowercase to ensure consistent and case-insensitive processing.

2. **Remove all punctuation**: Eliminate any punctuation marks (e.g., periods, commas, exclamation marks) from the text to avoid interference in language analysis.

3. **Remove all numbers**: Eliminate all numeric characters from the text since they might not be relevant for certain natural language processing tasks.

4. **Remove all words with 2 or more Xs in a row**: Remove any words that contain two or more consecutive occurrences of the letter "X" as they may not contribute meaningful information.

5. **Remove the bottom and top 2% of text by length**: Discard the shortest and longest text samples, removing the bottom 2% and top 2% of the text's length, respectively. This step is aimed at reducing the impact of outliers and ensuring a more balanced dataset.
task_categories:image-to-text size_categories:1K<n<10K language:en medical region:us
{"language": ["en"], "size_categories": ["1K<n<10K"], "task_categories": ["image-to-text"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "text", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 1108945726.54, "num_examples": 6060}], "download_size": 1108991167, "dataset_size": 1108945726.54}, "tags": ["medical"]}
TAGS
#task_categories-image-to-text #size_categories-1K<n<10K #language-English #medical #region-us 

# Indiana University Chest Xray Dataset Card

## Data sources:
This is a converted and processed version of the open access pneumonia chest x-ray dataset provided by the indiana university.<br>
You can see its information page here.<br>
The compressed images in the png format were downloaded from here and the corresponding reports from here.

## Data fields:
There are two fields: image and text.
The images are the x-rays and the texts are their associated findings.

## Preprocessing done:

1. Make all text lowercase: Convert all text to lowercase to ensure consistent and case-insensitive processing.

2. Remove all punctuation: Eliminate any punctuation marks (e.g., periods, commas, exclamation marks) from the text to avoid interference in language analysis.

3. Remove all numbers: Eliminate all numeric characters from the text since they might not be relevant for certain natural language processing tasks.

4. Remove all words with 2 or more Xs in a row: Remove any words that contain two or more consecutive occurrences of the letter "X" as they may not contribute meaningful information.

5. Remove the bottom and top 2% of text by length: Discard the shortest and longest text samples, removing the bottom 2% and top 2% of the text's length, respectively. This step is aimed at reducing the impact of outliers and ensuring a more balanced dataset.
# Indiana University Chest Xray Dataset Card ## Data sources:
This is a converted and processed version of the open access pneumonia chest x-ray dataset provided by the indiana university.<br>
You can see its information page here.<br>
The compressed images in the png format were downloaded from here and the corresponding reports from here. ## Data fields:
There are two fields: image and text.
The images are the x-rays and the texts are their associated findings. ## Preprocessing done:

1. Make all text lowercase: Convert all text to lowercase to ensure consistent and case-insensitive processing.

2. Remove all punctuation: Eliminate any punctuation marks (e.g., periods, commas, exclamation marks) from the text to avoid interference in language analysis.

3. Remove all numbers: Eliminate all numeric characters from the text since they might not be relevant for certain natural language processing tasks.

4. Remove all words with 2 or more Xs in a row: Remove any words that contain two or more consecutive occurrences of the letter "X" as they may not contribute meaningful information.

5. Remove the bottom and top 2% of text by length: Discard the shortest and longest text samples, removing the bottom 2% and top 2% of the text's length, respectively. This step is aimed at reducing the impact of outliers and ensuring a more balanced dataset.
TAGS
#task_categories-image-to-text #size_categories-1K<n<10K #language-English #medical #region-us 
 # Indiana University Chest Xray Dataset Card ## Data sources:
This is a converted and processed version of the open access pneumonia chest x-ray dataset provided by the indiana university.<br>
You can see its information page here.<br>
The compressed images in the png format were downloaded from here and the corresponding reports from here. ## Data fields:
There are two fields: image and text.
The images are the x-rays and the texts are their associated findings. ## Preprocessing done:

1. Make all text lowercase: Convert all text to lowercase to ensure consistent and case-insensitive processing.

2. Remove all punctuation: Eliminate any punctuation marks (e.g., periods, commas, exclamation marks) from the text to avoid interference in language analysis.

3. Remove all numbers: Eliminate all numeric characters from the text since they might not be relevant for certain natural language processing tasks.

4. Remove all words with 2 or more Xs in a row: Remove any words that contain two or more consecutive occurrences of the letter "X" as they may not contribute meaningful information.

5. Remove the bottom and top 2% of text by length: Discard the shortest and longest text samples, removing the bottom 2% and top 2% of the text's length, respectively. This step is aimed at reducing the impact of outliers and ensuring a more balanced dataset.