Datasets:
Tasks:
Visual Question Answering
Formats:
parquet
Languages:
English
Size:
10K - 100K
ArXiv:
Tags:
medical
License:
"""PathVQA: 30000+ Questions for Medical Visual Question Answering""" | |
import os | |
import pandas | |
import datasets | |
_CITATION = """\ | |
@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} | |
} | |
""" | |
_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. | |
""" | |
_HOMEPAGE = "https://github.com/UCSD-AI4H/PathVQA" | |
_LICENSE = "MIT" | |
_URLS = { | |
"train": "data/train.parquet", | |
"val": "data/val.parquet", | |
"test": "data/test.parquet", | |
} | |
class PathVQA(datasets.GeneratorBasedBuilder): | |
""" | |
PathVQA: 30000+ Questions for Medical Visual Question Answering. | |
The data was obtained from the updated Google Drive link shared by the authors on Feb 15, 2023, | |
see https://github.com/UCSD-AI4H/PathVQA/commit/117e7f4ef88a0e65b0e7f37b98a73d6237a3ceab. 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. | |
""" | |
VERSION = datasets.Version("0.1.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"image": datasets.Image(), | |
"question": datasets.Value("string"), | |
"answer": datasets.Value("string") | |
} | |
), | |
homepage=_HOMEPAGE, | |
license=_LICENSE, | |
citation=_CITATION, | |
) | |
def _split_generators(self, dl_manager): | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_manager.download(_URLS["train"])), | |
"split": "train", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.VALIDATION, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_manager.download(_URLS["val"])), | |
"split": "val", | |
}, | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
"filepath": os.path.join(dl_manager.download(_URLS["test"])), | |
"split": "test" | |
}, | |
), | |
] | |
def _generate_examples(self, filepath, split): | |
df = pandas.read_parquet(filepath) | |
for key, row in df.iterrows(): | |
yield key, { | |
"image": row["image"], | |
"question": row["question"], | |
"answer": row["answer"] | |
} | |