{"url":"/dataset/chaoyang","name":"Chaoyang","full_name":null,"description_markdown":"Chaoyang dataset contains 1111 normal, 842 serrated, 1404 adenocarcinoma, 664 adenoma, and 705 normal, 321 serrated, 840 adenocarcinoma, 273 adenoma samples for training and testing, respectively. This noisy dataset is constructed in the real scenario.  \r\n\r\n- Details: Colon slides from Chaoyang hospital, the patch size is 512 × 512. We invited 3 professional pathologists to label the patches, respectively. We took the parts of labeled patches with consensus results from 3 pathologists as the testing set. Others we used as the training set. For the samples with inconsistent labeling opinions of the three doctors in the training set (this part accounts for about 40%), we randomly selected the opinions from one of the three doctors.\r\n\r\n- The original WSIs are scanned at X20 objective magnification.","description_withheld":null,"homepage":"https://bupt-ai-cz.github.io/HSA-NRL/","introduced_date":"2021-12-05","introduced_date_note":null,"introduced_by":{"paper":"/paper/hard-sample-aware-noise-robust-learning-for","title":"Hard Sample Aware Noise Robust Learning for Histopathology Image Classification","first_author":"Chuang Zhu","url":null},"license":{"name":"This dataset is made freely available to academic and non-academic entities for non-commercial purposes such as academic research, teaching, scientific publications, or personal experimentation. Permission is granted to use the data given that you agree to our license terms in \"https://github.com/bupt-ai-cz/HSA-NRL\"","url":"https://github.com/bupt-ai-cz/HSA-NRL"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"},{"name":"Learning with noisy labels","url":"/task/learning-with-noisy-labels","datasets_with_task":"/datasets/task/learning-with-noisy-labels"},{"name":"Colon Cancer Detection In Confocal Laser Microscopy Images","url":"/task/colon-cancer-detection-in-confocal-laser","datasets_with_task":"/datasets/task/colon-cancer-detection-in-confocal-laser"},{"name":"Histopathological Image Classification","url":"/task/histopathological-image-classification","datasets_with_task":"/datasets/task/histopathological-image-classification"}],"languages":[{"name":"English","url":"/datasets/language/english"},{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["Chaoyang"],"data_loaders":[{"repo":"https://github.com/bupt-ai-cz/HSA-NRL","url":"https://github.com/bupt-ai-cz/HSA-NRL","frameworks":["pytorch"]}],"num_papers_in_archive":19,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-chaoyang","task":"Image Classification","dataset_variant":"Chaoyang","rows":1,"metrics":["Accuracy"],"first_row_in_archive_order":{"model":"HSANR","paper":"/paper/hard-sample-aware-noise-robust-learning-for","metrics":{"Accuracy":"83.4"},"code_links":[{"title":"bupt-ai-cz/HSA-NRL","url":"https://github.com/bupt-ai-cz/HSA-NRL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/learning-with-noisy-labels-on-chaoyang","task":"Learning with noisy labels","dataset_variant":"Chaoyang","rows":1,"metrics":["ACCURACY"],"first_row_in_archive_order":{"model":"HSANR","paper":"/paper/hard-sample-aware-noise-robust-learning-for","metrics":{"ACCURACY":"83.4"},"code_links":[{"title":"bupt-ai-cz/HSA-NRL","url":"https://github.com/bupt-ai-cz/HSA-NRL"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/hard-sample-aware-noise-robust-learning-for","title":"Hard Sample Aware Noise Robust Learning for Histopathology Image Classification","date":"2021-12-05","rows_on_this_dataset":2,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}