o Ni@sdZddlmZddlmZddlmZddlZddlZddlZddlm m Z ddl m ZdZdZdZejed Zejed Zejed Zejed Zed ddddZGdddejjZdS)z Chexpert.)absolute_import)division)print_functionNa1CheXpert is a large dataset of chest X-rays and competition for automated chest x-ray interpretation, which features uncertainty labels and radiologist-labeled reference standard evaluation sets. It consists of 224,316 chest radiographs of 65,240 patients, where the chest radiographic examinations and the associated radiology reports were retrospectively collected from Stanford Hospital. Each report was labeled for the presence of 14 observations as positive, negative, or uncertain. We decided on the 14 observations based on the prevalence in the reports and clinical relevance. The CheXpert dataset must be downloaded separately after reading and agreeing to a Research Use Agreement. To do so, please follow the instructions on the website, https://stanfordmlgroup.github.io/competitions/chexpert/. a@article{DBLP:journals/corr/abs-1901-07031, author = {Jeremy Irvin and Pranav Rajpurkar and Michael Ko and Yifan Yu and Silviana Ciurea{-}Ilcus and Chris Chute and Henrik Marklund and Behzad Haghgoo and Robyn L. Ball and Katie Shpanskaya and Jayne Seekins and David A. Mong and Safwan S. Halabi and Jesse K. Sandberg and Ricky Jones and David B. Larson and Curtis P. Langlotz and Bhavik N. Patel and Matthew P. Lungren and Andrew Y. Ng}, title = {CheXpert: {A} Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison}, journal = {CoRR}, volume = {abs/1901.07031}, year = {2019}, url = {http://arxiv.org/abs/1901.07031}, archivePrefix = {arXiv}, eprint = {1901.07031}, timestamp = {Fri, 01 Feb 2019 13:39:59 +0100}, biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-07031}, bibsource = {dblp computer science bibliography, https://dblp.org} } zCheXpert-v1.0-smalltrainvalidz train.csvz valid.csv uncertainpositivenegative unmentioned)z-1.0z1.0z0.0c@s8eZdZdZejdZdZddZ ddZ dd Z d S) ChexpertzCheXpert 2019.z3.1.0aA You must register and agree to user agreement on the dataset page: https://stanfordmlgroup.github.io/competitions/chexpert/ Afterwards, you have to put the CheXpert-v1.0-small directory in the manual_dir. It should contain subdirectories: train/ and valid/ with images and also train.csv and valid.csv files. c CsZtjj|ttjtjtjtjtjj t dtjj ddgddddt dS)N)namesfrontallateralnameimagelabel image_view)rrz8https://stanfordmlgroup.github.io/competitions/chexpert/)builder descriptionfeaturessupervised_keyshomepagecitation) tfdscore DatasetInfo _DESCRIPTIONr FeaturesDictTextImageSequence ClassLabel_LABELSvalues _CITATION)selfr(e/home/ubuntu/.local/lib/python3.10/site-packages/tensorflow_datasets/image_classification/chexpert.py_info[s"zChexpert._infocCs|j}tj|t}tj|t}tjj |rtjj |s'd|}t |t j j t jj|tj|tddt j j t jj|tj|tddgS)zReturns SplitGenerators.zXYou must download the dataset folder from CheXpertwebsite manually and place it into %s.) imgs_pathcsv_path)r gen_kwargs) manual_dirospathjoin _TRAIN_DIR_VALIDATION_DIRtfiogfileexistsAssertionErrorrrSplitGeneratorSplitTRAIN_TRAIN_LABELS_FNAME VALIDATION_VALIDATION_LABELS_FNAME)r' dl_managerr0 train_pathval_pathmsgr(r(r)_split_generatorsls*  zChexpert._split_generatorsc #stjj|<}t|}|jdd}|D]$d}fdd|D}d}||tj ||||dfVqWddS1sGwYdS)zYields examples.NPathcsg|]}t|qSr()r$).0keyrowr(r) sz/Chexpert._generate_examples..zFrontal/Lateralr) r4r5r6GFilecsv DictReader fieldnameslowerr/r0r1) r'r+r,csv_freader label_keysrlabelsrr(rHr)_generate_exampless     "zChexpert._generate_examplesN) __name__ __module__ __qualname____doc__rrVersionVERSIONMANUAL_DOWNLOAD_INSTRUCTIONSr*rCrTr(r(r(r)r Ns  r )rX __future__rrr collectionsrLr/tensorflow.compat.v2compatv2r4tensorflow_datasets.public_api public_apirrr& _DATA_DIRr0r1r2r3r<r> OrderedDictr$rGeneratorBasedBuilderr r(r(r(r)s.