CheXpert는 흉부 X선과 자동화된 흉부 X선 해석을 위한 경쟁의 대규모 데이터 세트로, 불확실성 레이블과 방사선 전문의 레이블 참조 표준 평가 세트를 특징으로 합니다. 65,240명의 환자에 대한 224,316장의 흉부 방사선 사진으로 구성되어 있으며 흉부 방사선 검사 및 관련 방사선 보고서는 Stanford 병원에서 후향적으로 수집되었습니다. 각 보고서는 14개의 관찰이 있는지에 대해 긍정적, 부정적 또는 불확실한 것으로 레이블이 지정되었습니다. 보고서의 유병률과 임상적 관련성을 기반으로 14개의 관찰 결과를 결정했습니다.
수동 다운로드 지침 : 이 데이터 세트는 원본 데이터를 download_config.manual_dir에 수동으로 download_config.manual_dir 해야 합니다(기본값은 ~/tensorflow_datasets/downloads/manual/ ). 데이터 세트 페이지에서 등록 하고 사용자 계약에 동의해야 합니다. 여기에는 이미지가 포함된 train/ 및 valid/와 train.csv 및 valid.csv 파일이 포함된 하위 디렉터리가 포함되어야 합니다.
@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}
}
[null,null,["최종 업데이트: 2022-12-06(UTC)"],[],[],null,["# chexpert\n\n\u003cbr /\u003e\n\n| **Warning:** Manual download required. See instructions below.\n\n- **Description**:\n\nCheXpert is a large dataset of chest X-rays and competition for automated chest\nx-ray interpretation, which features uncertainty labels and radiologist-labeled\nreference standard evaluation sets. It consists of 224,316 chest radiographs of\n65,240 patients, where the chest radiographic examinations and the associated\nradiology reports were retrospectively collected from Stanford Hospital. Each\nreport was labeled for the presence of 14 observations as positive, negative, or\nuncertain. We decided on the 14 observations based on the prevalence in the\nreports and clinical relevance.\n\nThe CheXpert dataset must be downloaded separately after reading and agreeing to\na Research Use Agreement. To do so, please follow the instructions on the\nwebsite, \u003chttps://stanfordmlgroup.github.io/competitions/chexpert/\u003e\n\n- **Additional Documentation** :\n [Explore on Papers With Code\n north_east](https://paperswithcode.com/dataset/chexpert)\n\n- **Homepage** :\n \u003chttps://stanfordmlgroup.github.io/competitions/chexpert/\u003e\n\n- **Source code** :\n [`tfds.image_classification.Chexpert`](https://github.com/tensorflow/datasets/tree/master/tensorflow_datasets/image_classification/chexpert.py)\n\n- **Versions**:\n\n - **`3.1.0`** (default): No release notes.\n- **Download size** : `Unknown size`\n\n- **Dataset size** : `Unknown size`\n\n- **Manual download instructions** : This dataset requires you to\n download the source data manually into `download_config.manual_dir`\n (defaults to `~/tensorflow_datasets/downloads/manual/`): \n\n You must register and agree to user agreement on the dataset page:\n \u003chttps://stanfordmlgroup.github.io/competitions/chexpert/\u003e\n Afterwards, you have to put the CheXpert-v1.0-small directory in the\n manual_dir. It should contain subdirectories: train/ and valid/ with images\n and also train.csv and valid.csv files.\n\n- **Auto-cached**\n ([documentation](https://www.tensorflow.org/datasets/performances#auto-caching)):\n Unknown\n\n- **Splits**:\n\n| Split | Examples |\n|-------|----------|\n\n- **Feature structure**:\n\n FeaturesDict({\n 'image': Image(shape=(None, None, 3), dtype=uint8),\n 'image_view': ClassLabel(shape=(), dtype=int64, num_classes=2),\n 'label': Sequence(ClassLabel(shape=(), dtype=int64, num_classes=4)),\n 'name': Text(shape=(), dtype=string),\n })\n\n- **Feature documentation**:\n\n| Feature | Class | Shape | Dtype | Description |\n|------------|----------------------|-----------------|--------|-------------|\n| | FeaturesDict | | | |\n| image | Image | (None, None, 3) | uint8 | |\n| image_view | ClassLabel | | int64 | |\n| label | Sequence(ClassLabel) | (None,) | int64 | |\n| name | Text | | string | |\n\n- **Supervised keys** (See\n [`as_supervised` doc](https://www.tensorflow.org/datasets/api_docs/python/tfds/load#args)):\n `('image', 'label')`\n\n- **Figure**\n ([tfds.show_examples](https://www.tensorflow.org/datasets/api_docs/python/tfds/visualization/show_examples)):\n Not supported.\n\n- **Examples**\n ([tfds.as_dataframe](https://www.tensorflow.org/datasets/api_docs/python/tfds/as_dataframe)):\n Missing.\n\n- **Citation**:\n\n @article{DBLP:journals/corr/abs-1901-07031,\n 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},\n title = {CheXpert: {A} Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison},\n journal = {CoRR},\n volume = {abs/1901.07031},\n year = {2019},\n url = {http://arxiv.org/abs/1901.07031},\n archivePrefix = {arXiv},\n eprint = {1901.07031},\n timestamp = {Fri, 01 Feb 2019 13:39:59 +0100},\n biburl = {https://dblp.org/rec/bib/journals/corr/abs-1901-07031},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n }"]]
