Datasets › GasHisSDB
GasHisSDB
Four pathologists from Longhua Hospital Shanghai University of Traditional Chinese Medicine provide 600 images of gastric cancer pathology images at size 2048×2048 pixels. These images were scanned using a NewUsbCamera and digitized at ×20 magnification, tissue-level labels were also given by the four experienced pathologists. Based on that, five biomedical researchers from Northeastern University cropped them to 245,196 sub-sized gastric cancer pathology images, and two experienced pathologists from Liaoning Cancer Hospital and Institute perform the calibration. The 245,196 images were split to three sizes (160×160, 120×120, 80×80) for two categories: abnormal and normal.
Benchmarks archive 2025-07-28
All 1 leaderboard 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 | GasHisSDB | CoAtNet-1 Accuracy 98.74 | CoAtNet: Marrying Convolution and Attention for All Data Sizes | rwightman/pytorch-image-models +13 | 8 | Compare |
Papers archive 2025-07-28
7 shown of 7 papers 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 11. 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 | |||
|---|---|---|---|---|
| CoAtNet: Marrying Convolution and Attention for All Data Sizes | 14 | 1 | 9 Jun 2021 | ran 2 of 5 samples (3 unverified; 1 pointer-only for licence) |
| RegNet: Self-Regulated Network for Image Classification | 14 | 1 | 3 Jan 2021 | not harvested |
| EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks | 144 | 1 | 28 May 2019 | ran 171 of 302 samples (131 unverified; 112 pointer-only for licence) |
| Res2Net: A New Multi-scale Backbone Architecture | 34 | 1 | 2 Apr 2019 | ran 3 of 9 samples (6 unverified; 9 pointer-only for licence) |
| Aggregated Residual Transformations for Deep Neural Networks | 61 | 1 | 16 Nov 2016 | ran 34 of 80 samples (46 unverified; 13 pointer-only for licence) |
| Densely Connected Convolutional Networks | 146 | 1 | 25 Aug 2016 | ran 18 of 71 samples (53 unverified; 7 pointer-only for licence) |
| Deep Residual Learning for Image Recognition | 484 | 2 | 10 Dec 2015 | ran 230 of 377 samples (147 unverified; 187 pointer-only for licence) |
Dataset loaders archive 2025-07-28
No loader listed in the archive.
Tasks archive 2025-07-28
License archive 2025-07-28
No licence recorded in the archive. Absence here is not a statement about the dataset's terms.
Modalities archive 2025-07-28
Languages archive 2025-07-28
No language tagged.
Variants archive 2025-07-28
- GasHisSDB
1 variant name, as the archive lists them.
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