Datasets:

alkzar90
/

NIH-Chest-X-ray-dataset

	# Copyright 2022 Cristóbal Alcázar
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.
	"""NIH Chest X-ray Dataset"""


	import os
	import datasets

	from datasets.tasks import ImageClassification
	from requests import get
	from pandas import read_csv

	logger = datasets.logging.get_logger(__name__)

	_CITATION = """\
	@inproceedings{Wang_2017,
	doi = {10.1109/cvpr.2017.369},
	url = {https://doi.org/10.1109%2Fcvpr.2017.369},
	year = 2017,
	month = {jul},
	publisher = {{IEEE}
	},
	author = {Xiaosong Wang and Yifan Peng and Le Lu and Zhiyong Lu and Mohammadhadi Bagheri and Ronald M. Summers},
	title = {{ChestX}-Ray8: Hospital-Scale Chest X-Ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases},
	booktitle = {2017 {IEEE} Conference on Computer Vision and Pattern Recognition ({CVPR})}
	}
	"""


	_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
	"""


	_HOMEPAGE = "https://nihcc.app.box.com/v/chestxray-nihcc"


	_REPO = "https://huggingface.co/datasets/alkzar90/NIH-Chest-X-ray-dataset/resolve/main/data"


	_IMAGE_URLS = [
	f"{_REPO}/images/images_001.zip",
	f"{_REPO}/images/images_002.zip",
	f"{_REPO}/images/images_003.zip",
	f"{_REPO}/images/images_004.zip",
	f"{_REPO}/images/images_005.zip",
	f"{_REPO}/images/images_006.zip",
	f"{_REPO}/images/images_007.zip",
	f"{_REPO}/images/images_008.zip",
	f"{_REPO}/images/images_009.zip",
	f"{_REPO}/images/images_010.zip",
	f"{_REPO}/images/images_011.zip",
	f"{_REPO}/images/images_012.zip"
	#'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": f"{_REPO}/train_val_list.txt",
	"test_list": f"{_REPO}/test_list.txt",
	"labels": f"{_REPO}/Data_Entry_2017_v2020.csv",
	"BBox": f"{_REPO}/BBox_List_2017.csv",
	"image_urls": _IMAGE_URLS
	}


	_LABEL2IDX = {"No Finding": 0,
	"Atelectasis": 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 ChestXray14Config(datasets.BuilderConfig):
	"""NIH Image Chest X-ray14 configuration."""

	def __init__(self, name, **kwargs):
	super(ChestXray14Config, self).__init__(
	version=datasets.Version("1.0.0"),
	name=name,
	description="NIH ChestX-ray14",
	**kwargs,
	)



	class ChestXray14(datasets.GeneratorBasedBuilder):
	"""NIH Image Chest X-ray14 dataset."""


	BUILDER_CONFIGS = [
	ChestXray14Config("image-classification"),
	ChestXray14Config("object-detection"),
	]

	def _info(self):
	if self.config.name == "image-classification":
	features = datasets.Features(
	{
	"image": datasets.Image(),
	"labels": datasets.features.Sequence(
	datasets.features.ClassLabel(
	num_classes=len(_NAMES),
	names=_NAMES
	)
	),
	}
	)
	keys = ("image", "labels")


	if self.config.name == "object-detection":
	features = datasets.Features(
	{
	"image_id": datasets.Value("string"),
	"patient_id": datasets.Value("int32"),
	"image": datasets.Image(),
	"width": datasets.Value("int32"),
	"height": datasets.Value("int32"),
	}
	)
	object_dict = {
	"image_id": datasets.Value("string"),
	"area": datasets.Value("int64"),
	"bbox": datasets.Sequence(datasets.Value("float32"), length=4),
	}
	features["objects"] = [object_dict]
	keys = ("image", "objects")



	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=features,
	supervised_keys=keys,
	homepage=_HOMEPAGE,
	citation=_CITATION,
	)


	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 os.path.basename(img) in train_val_list:
	train_files.append(img)
	else:
	test_files.append(img)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"files": train_files
	}

	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"files": test_files
	}
	)
	]

	def _generate_examples(self, files):

	if self.config.name == "image-classification":
	# 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
	image_labels = label_csv[label_csv["Image Index"] == image_id]["Finding Labels"].values[0].split("\|")
	if file_name.endswith(".png"):
	yield i, {
	"image": path,
	"labels": image_labels,
	}