CheXpert は胸部 X 線の大規模なデータセットであり、自動化された胸部 X 線解釈の競合であり、不確実性ラベルと放射線科医がラベル付けした参照標準評価セットを特徴としています。これは、65,240 人の患者の 224,316 枚の胸部 X 線写真で構成されており、胸部 X 線検査と関連する放射線レポートがスタンフォード病院から遡及的に収集されました。各レポートは、14 の観察結果の存在について、陽性、陰性、または不確実としてラベル付けされました。レポートの有病率と臨床的関連性に基づいて、14の観察結果を決定しました。
@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,["最終更新日 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 }"]]
