Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
import os | |
import datasets | |
from datasets.tasks import ImageClassification | |
from requests import get | |
from pandas import read_csv | |
logger = datasets.logging.get_logger(__name__) | |
_HOMEPAGE = "https://nihcc.app.box.com/v/ChestXray-NIHCC" | |
_CITATION = """\ | |
@ONLINE {beansdata, | |
author="Xiaosong Wang, Yifan Peng, Le Lu, Zhiyong Lu, Mohammadhadi Bagheri, Ronald Summer", | |
title="ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases", | |
month="January", | |
year="2017", | |
url="https://nihcc.app.box.com/v/ChestXray-NIHCC" | |
} | |
""" | |
_DESCRIPTION = """\ | |
The NIH Chest X-ray dataset consists of 100,000 de-identified images of chest x-rays. The images are in PNG format. | |
The data is provided by the NIH Clinical Center and is available through the NIH download site: https://nihcc.app.box.com/v/ChestXray-NIHCC | |
""" | |
_IMAGE_URLS2 = [ | |
'https://nihcc.box.com/shared/static/vfk49d74nhbxq3nqjg0900w5nvkorp5c.gz', | |
'https://nihcc.box.com/shared/static/i28rlmbvmfjbl8p2n3ril0pptcmcu9d1.gz', | |
'https://nihcc.box.com/shared/static/f1t00wrtdk94satdfb9olcolqx20z2jp.gz', | |
'https://nihcc.box.com/shared/static/0aowwzs5lhjrceb3qp67ahp0rd1l1etg.gz', | |
'https://nihcc.box.com/shared/static/v5e3goj22zr6h8tzualxfsqlqaygfbsn.gz', | |
'https://nihcc.box.com/shared/static/asi7ikud9jwnkrnkj99jnpfkjdes7l6l.gz', | |
'https://nihcc.box.com/shared/static/jn1b4mw4n6lnh74ovmcjb8y48h8xj07n.gz', | |
'https://nihcc.box.com/shared/static/tvpxmn7qyrgl0w8wfh9kqfjskv6nmm1j.gz', | |
'https://nihcc.box.com/shared/static/upyy3ml7qdumlgk2rfcvlb9k6gvqq2pj.gz', | |
'https://nihcc.box.com/shared/static/l6nilvfa9cg3s28tqv1qc1olm3gnz54p.gz', | |
'https://nihcc.box.com/shared/static/hhq8fkdgvcari67vfhs7ppg2w6ni4jze.gz', | |
'https://nihcc.box.com/shared/static/ioqwiy20ihqwyr8pf4c24eazhh281pbu.gz' | |
] | |
_IMAGE_URLS = [ | |
'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_001.tar.gz', | |
'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/dummy/0.0.0/images_002.tar.gz' | |
] | |
_URLS = { | |
'train_val_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/train_val_list.txt', | |
'test_list': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/test_list.txt', | |
'labels': 'https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/raw/main/data/Data_Entry_2017_v2020.csv', | |
'image_urls': _IMAGE_URLS | |
} | |
_LABEL2IDX = {'No Finding': 0, | |
'Atelactasis': 1, | |
'Cardiomegaly': 2, | |
'Effusion': 3, | |
'Infiltration': 4, | |
'Mass': 5, | |
'Nodule': 6, | |
'Pneumonia': 7, | |
'Pneumothorax': 8, | |
'Consolidation': 9, | |
'Edema': 10, | |
'Emphysema': 11, | |
'Fibrosis': 12, | |
'Pleural_Thickening': 13, | |
'Hernia': 14} | |
_NAMES = list(_LABEL2IDX.keys()) | |
class XChest(datasets.GeneratorBasedBuilder): | |
"""NIH Image Chest X-ray dataset.""" | |
VERSION = datasets.Version("0.0.0") | |
def _info(self): | |
return datasets.DatasetInfo( | |
description=_DESCRIPTION, | |
features=datasets.Features( | |
{ | |
"image_file_path": datasets.Value("string"), | |
"image": datasets.Image(), | |
#"labels": datasets.features.ClassLabel(names=_NAMES), | |
"labels": datasets.features.Sequence( | |
datasets.features.ClassLabel(num_classes=len(_NAMES), | |
names=_NAMES) | |
) | |
} | |
), | |
supervised_keys=("image", "labels"), | |
homepage=_HOMEPAGE, | |
citation=_CITATION, | |
#task_templates=[ImageClassification(image_column="image", | |
# label_column="labels")], | |
) | |
def _split_generators(self, dl_manager): | |
# Get the image names that belong to the train-val dataset | |
logger.info("Downloading the train_val_list image names") | |
train_val_list = get(_URLS['train_val_list']).iter_lines() | |
train_val_list = set([x.decode('UTF8') for x in train_val_list]) | |
logger.info(f"Check train_val_list: {train_val_list}") | |
# Create list for store the name of the images for each dataset | |
train_files = [] | |
test_files = [] | |
# Download batches | |
data_files = dl_manager.download_and_extract(_URLS['image_urls']) | |
# Iterate trought image folder and check if they belong to | |
# the trainset or testset | |
for batch in data_files: | |
logger.info(f"Batch for data_files: {batch}") | |
path_files = dl_manager.iter_files(batch) | |
for img in path_files: | |
if img.split('/')[-1] in train_val_list: | |
train_files.append(img) | |
else: | |
test_files.append(img) | |
return [ | |
datasets.SplitGenerator( | |
name=datasets.Split.TRAIN, | |
gen_kwargs={ | |
'files': iter(train_files) | |
} | |
), | |
datasets.SplitGenerator( | |
name=datasets.Split.TEST, | |
gen_kwargs={ | |
'files': iter(test_files) | |
} | |
) | |
] | |
def _generate_examples(self, files): | |
# Read csv with image labels | |
label_csv = read_csv(_URLS['labels']) | |
for i, path in enumerate(files): | |
file_name = os.path.basename(path) | |
# Get image id to filter the respective row of the csv | |
image_id = file_name.split('/')[-1] | |
image_labels = label_csv[label_csv['Image Index'] == image_id]['Finding Labels'].values[0].split('|') | |
if file_name.endswith(".png"): | |
yield i, { | |
"image_file_path": path, | |
"image": path, | |
"labels": image_labels, | |
} | |