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
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 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 LeaderboardsThis 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.
LanguagesThe question-answer pairs are in English.
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' }
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 |
The authors have released the dataset under the CC0 1.0 Universal License.
@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} }