Datasets › Chaoyang

Chaoyang

Introduced by Chuang Zhu et al. in Hard Sample Aware Noise Robust Learning for Histopathology Image Classification5 Dec 2021 archive 2025-07-28

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.

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.
The original WSIs are scanned at X20 objective magnification.

Homepage Benchmarks Papers JSON

Benchmarks archive 2025-07-28

All 2 leaderboards whose dataset resolves to this page shown (sort by any header). "First row" is the archive's own first row at snapshot, in the archive's row order; nothing here re-ranks and metric direction is not asserted.

		First row (archive order)	Paper	Code
Image Classification	Chaoyang	HSANR Accuracy 83.4	Hard Sample Aware Noise Robust Learning for...	bupt-ai-cz/HSA-NRL	1	Compare
Learning with noisy labels	Chaoyang	HSANR ACCURACY 83.4	Hard Sample Aware Noise Robust Learning for...	bupt-ai-cz/HSA-NRL	1	Compare

Papers archive 2025-07-28

1 shown of 1 paper with a leaderboard row on this dataset's benchmarks, newest first. The archive's own "papers using this dataset" list was never published, so this is the benchmark-backed subset; the archive's count for this dataset is 19. The Syntology column is from Syntology's graph (read 2026-09-24), stated per sample; it is not part of any archive number.

			Date	Samples run Syntology
Hard Sample Aware Noise Robust Learning for Histopathology Image Classification	1	2	5 Dec 2021	not harvested

Dataset loaders archive 2025-07-28

bupt-ai-cz/HSA-NRLpytorch

1 loader as listed in the archive; links are outbound and not re-checked here.

Tasks archive 2025-07-28

Image Classification Learning with noisy labels Colon Cancer Detection In Confocal Laser Microscopy Images Histopathological Image Classification

License archive 2025-07-28

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"

Modalities archive 2025-07-28

Images Medical

Languages archive 2025-07-28

English Chinese

Variants archive 2025-07-28

Chaoyang

1 variant name, as the archive lists them.

At a glance archive 2025-07-28

Introduced: 5 Dec 2021
Papers: 19
Benchmarks: 2
With a result: 1
Licence: 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"
Modalities: Images, Medical
Languages: English, Chinese
Loaders: 1
Homepage: bupt-ai-cz.github.io/HSA-NRL/

Samples run Syntology

Of the 1 paper with a result here, 0 have harvested code samples in Syntology's graph. Summed over those: Syntology ran 0 of 0 samples; 0 unverified. 0 of the 0 have no sample that ran. Each row in the Papers table states its own "ran N of M samples" with the unverified count; a sample that ran executed on a synthesized fixture, which is not a correctness claim about any result. Read from the graph 2026-09-24.

Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections