metadata

license: mit
task_categories:
  - visual-question-answering
language:
  - en
tags:
  - medical
pretty_name: PathVQA
size_categories:
  - 10K<n<100K

Dataset Card for PathVQA

Dataset Description

PathVQA is a dataset of question-answer pairs on pathology images. The dataset is intended to be used for training and testing Medical Visual Question Answering (VQA) systems. The questions contained in the dataset are similar to those in the American Board of Pathology (ABP) test. The dataset includes both open-ended questions and binary "yes/no" questions. The dataset is built from two publicly-available pathology textbooks: "Textbook of Pathology" and "Basic Pathology", and a publicly-available digital library: "Pathology Education Informational Resource" (PEIR). The copyrights of images and captions belong to the publishers and authors of these two books, and the owners of the PEIR digital library.

Repository: PathVQA Official GitHub Repository
Paper: PathVQA: 30000+ Questions for Medical Visual Question Answering
Leaderboard: Papers with Code Leaderboard

Dataset Summary

The data was obtained from the updated Google Drive link shared by the authors on Feb 15, 2023, see the commit in the GitHub repository. This version of the dataset contains a total of 5,004 images and 32,795 question-answer pairs. Out of the 5,004 images, 4,289 images are referenced by a question-answer pair, while 715 images are not used. There are a few image-question-answer triplets which occur more than once in the same split (training, validation, test). After dropping the duplicate image-question-answer triplets, the dataset contains 32,632 question-answer pairs on 4,289 images.

Supported Tasks and Leaderboards

This dataset has an active leaderboard which can be found on Papers with Code and ranks models based on three metrics: "Yes/No Accuracy", "Free-form accuracy" and "Overall accuracy". "Yes/No Accuracy" is the accuracy of a model's generated answers for the subset of binary "yes/no" questions. "Free-form 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': {'bytes': b'\xff\xd8\xff\xee\x00\x0eAdobe\x00d..., 'path': None},
  'question': 'What does immunoperoxidase staining reveal that marks positively with anti-CD4 antibodies?',
  'answer': 'a predominantly perivascular cellular infiltrate'
}

Data Fields

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

Data Splits

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

Additional Information

Licensing Information

The authors have released the dataset under the MIT License.

Citation Information

@article{he2020pathvqa,
    title={PathVQA: 30000+ Questions for Medical Visual Question Answering},
    author={He, Xuehai and Zhang, Yichen and Mou, Luntian and Xing, Eric and Xie, Pengtao},
    journal={arXiv preprint arXiv:2003.10286},
    year={2020}
}