Datasets:
Tasks:
Image Classification
Sub-tasks:
multi-class-image-classification
Languages:
English
Size:
100K<n<1M
ArXiv:
License:
# 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_003.zip", | |
# f"{_REPO}/images/images_004.zip", | |
# f"{_REPO}/images/images_005.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 == "image-classification": | |
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.Values("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 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): | |
if sef.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.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": path, | |
"labels": image_labels, | |
} | |