
# Dataset Card for VQA-RAD

## Dataset Description
VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing 
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions. 
The dataset is built from [MedPix](https://medpix.nlm.nih.gov/), which is a free open-access online database of medical images.
The question-answer pairs were manually generated by a team of clinicians.

**Homepage:** [Open Science Framework Homepage](https://osf.io/89kps/)<br>
**Paper:** [A dataset of clinically generated visual questions and answers about radiology images](https://www.nature.com/articles/sdata2018251)<br>
**Leaderboard:** [Papers with Code Leaderboard](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad)

### Dataset Summary
The dataset was downloaded from the [Open Science Framework Homepage](https://osf.io/89kps/) on June 3, 2023. The dataset contains 
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image 
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer  
triplet in common with the test set. After dropping these 4 image-question-answer triplets from the training set, the dataset contains 
2,244 question-answer pairs on 314 images.

#### Supported Tasks and Leaderboards
This dataset has an active leaderboard on [Papers with Code](https://paperswithcode.com/sota/medical-visual-question-answering-on-vqa-rad) 
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy 
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated 
answers across all questions.

#### Languages
The question-answer pairs are in English.

## Dataset Structure

### Data Instances
Each instance consists of an image-question-answer triplet.
```
{
  'image': <PIL.JpegImagePlugin.JpegImageFile image mode=RGB size=566x555>,
  'question': 'are regions of the brain infarcted?',
  'answer': 'yes'
}
```
### Data Fields
- `'image'`: the image referenced by the question-answer pair. 
- `'question'`: the question about the image.
- `'answer'`: the expected answer.

### Data Splits
The dataset is split into training and test. The split is provided directly by the authors.

|                         | Training Set |  Test Set |
|-------------------------|:------------:|:---------:|
| QAs                     |1,793         |451        |
| Images                  |313           |203        |

## Additional Information

### Licensing Information
The authors have released the dataset under the CC0 1.0 Universal License.

### Citation Information
```
@article{lau2018dataset,
    title={A dataset of clinically generated visual questions and answers about radiology images},
    author={Lau, Jason J and Gayen, Soumya and Ben Abacha, Asma and Demner-Fushman, Dina},
    journal={Scientific data},
    volume={5},
    number={1},
    pages={1--10},
    year={2018},
    publisher={Nature Publishing Group}
}
```
task_categories:visual-question-answering size_categories:1K<n<10K language:en license:cc0-1.0 medical region:us
{"language": ["en"], "license": "cc0-1.0", "size_categories": ["1K<n<10K"], "task_categories": ["visual-question-answering"], "paperswithcode_id": "vqa-rad", "pretty_name": "VQA-RAD", "tags": ["medical"], "dataset_info": {"features": [{"name": "image", "dtype": "image"}, {"name": "question", "dtype": "string"}, {"name": "answer", "dtype": "string"}], "splits": [{"name": "train", "num_bytes": 95883938.139, "num_examples": 1793}, {"name": "test", "num_bytes": 23818877.0, "num_examples": 451}], "download_size": 34496718, "dataset_size": 119702815.139}}
TAGS
#task_categories-visual-question-answering #size_categories-1K<n<10K #language-English #license-cc0-1.0 #medical #region-us 

Dataset Card for VQA-RAD
========================


Dataset Description
-------------------


VQA-RAD is a dataset of question-answer pairs on radiology images. The dataset is intended to be used for training and testing
Medical Visual Question Answering (VQA) systems. The dataset includes both open-ended questions and binary "yes/no" questions.
The dataset is built from MedPix, which is a free open-access online database of medical images.
The question-answer pairs were manually generated by a team of clinicians.


Homepage: Open Science Framework Homepage  

Paper: A dataset of clinically generated visual questions and answers about radiology images  

Leaderboard: Papers with Code Leaderboard


### Dataset Summary


The dataset was downloaded from the Open Science Framework Homepage on June 3, 2023. The dataset contains
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer  



#### Supported Tasks and Leaderboards


This dataset has an active leaderboard on Papers with Code
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions.


#### Languages


The question-answer pairs are in English.


Dataset Structure
-----------------


### Data Instances


Each instance consists of an image-question-answer triplet.


### Data Fields


* ''image'': the image referenced by the question-answer pair.
* ''question'': the question about the image.
* ''answer'': the expected answer.


### Data Splits


The dataset is split into training and test. The split is provided directly by the authors.



Additional Information
----------------------


### Licensing Information


The authors have released the dataset under the CC0 1.0 Universal License.



### Dataset Summary


The dataset was downloaded from the Open Science Framework Homepage on June 3, 2023. The dataset contains
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer #### Supported Tasks and Leaderboards


This dataset has an active leaderboard on Papers with Code
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions. #### Languages


The question-answer pairs are in English.


Dataset Structure
----------------- ### Data Instances


Each instance consists of an image-question-answer triplet. ### Data Fields


* ''image'': the image referenced by the question-answer pair.
* ''question'': the question about the image.
* ''answer'': the expected answer. ### Data Splits


The dataset is split into training and test. The split is provided directly by the authors.



Additional Information
---------------------- ### Licensing Information


The authors have released the dataset under the CC0 1.0 Universal License.
TAGS
#task_categories-visual-question-answering #size_categories-1K<n<10K #language-English #license-cc0-1.0 #medical #region-us 
 ### Dataset Summary


The dataset was downloaded from the Open Science Framework Homepage on June 3, 2023. The dataset contains
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer #### Supported Tasks and Leaderboards


This dataset has an active leaderboard on Papers with Code
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions. #### Languages


The question-answer pairs are in English.


Dataset Structure
----------------- ### Data Instances


Each instance consists of an image-question-answer triplet. ### Data Fields


* ''image'': the image referenced by the question-answer pair.
* ''question'': the question about the image.
* ''answer'': the expected answer. ### Data Splits


The dataset is split into training and test. The split is provided directly by the authors.



Additional Information
---------------------- ### Licensing Information


The authors have released the dataset under the CC0 1.0 Universal License.
passage: TAGS
#task_categories-visual-question-answering #size_categories-1K<n<10K #language-English #license-cc0-1.0 #medical #region-us 
### Dataset Summary


The dataset was downloaded from the Open Science Framework Homepage on June 3, 2023. The dataset contains
2,248 question-answer pairs and 315 images. Out of the 315 images, 314 images are referenced by a question-answer pair, while 1 image
is not used. The training set contains 3 duplicate image-question-answer triplets. The training set also has 1 image-question-answer#### Supported Tasks and Leaderboards


This dataset has an active leaderboard on Papers with Code
where models are ranked based on three metrics: "Close-ended Accuracy", "Open-ended accuracy" and "Overall accuracy". "Close-ended Accuracy" is
the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Open-ended accuracy" is the accuracy
of a model's generated answers for the subset of open-ended questions. "Overall accuracy" is the accuracy of a model's generated
answers across all questions.#### Languages


The question-answer pairs are in English.


Dataset Structure
-----------------### Data Instances


Each instance consists of an image-question-answer triplet.### Data Fields


* ''image'': the image referenced by the question-answer pair.
* ''question'': the question about the image.
* ''answer'': the expected answer.### Data Splits


The dataset is split into training and test. The split is provided directly by the authors.



Additional Information
----------------------### Licensing Information


The authors have released the dataset under the CC0 1.0 Universal License.