{"url":"/dataset/gashissdb","name":"GasHisSDB","full_name":null,"description_markdown":"Four pathologists from Longhua Hospital Shanghai University of Traditional Chinese Medicine provide 600 images of gastric cancer pathology images at size 2048$\\times$2048 pixels. These images were scanned using a NewUsbCamera and digitized at $\\times$20 magnification, tissue-level labels were also given by the four experienced pathologists. Based on that, five biomedical researchers from Northeastern University cropped them to 245,196 sub-sized gastric cancer pathology images, and two experienced pathologists from Liaoning Cancer Hospital and Institute perform the calibration. The 245,196 images were split to three sizes (160$\\times$160, 120$\\times$120, 80$\\times$80) for two categories: abnormal and normal.","description_withheld":null,"homepage":"https://gitee.com/neuhwm/GasHisSDB","introduced_date":"2021-06-04","introduced_date_note":null,"introduced_by":{"paper":"/paper/a-new-gastric-histopathology-subsize-image","title":"GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer","first_author":"Weiming Hu","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Image Classification","url":"/task/image-classification","datasets_with_task":"/datasets/task/image-classification"}],"languages":[],"variants":["GasHisSDB"],"data_loaders":[],"num_papers_in_archive":11,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/image-classification-on-gashissdb","task":"Image Classification","dataset_variant":"GasHisSDB","rows":8,"metrics":["Accuracy","Precision","F1-Score"],"first_row_in_archive_order":{"model":"CoAtNet-1","paper":"/paper/coatnet-marrying-convolution-and-attention","metrics":{"Accuracy":"98.74","F1-Score":"99.38","Precision":"99.97"},"code_links":[{"title":"rwightman/pytorch-image-models","url":"https://github.com/rwightman/pytorch-image-models"},{"title":"xmu-xiaoma666/External-Attention-pytorch","url":"https://github.com/xmu-xiaoma666/External-Attention-pytorch/blob/master/model/attention/CoAtNet.py"},{"title":"leondgarse/keras_cv_attention_models","url":"https://github.com/leondgarse/keras_cv_attention_models/tree/main/keras_cv_attention_models/coatnet"},{"title":"chinhsuanwu/coatnet-pytorch","url":"https://github.com/chinhsuanwu/coatnet-pytorch"},{"title":"canturan10/satellighte","url":"https://github.com/canturan10/satellighte"},{"title":"tyeso/Image_Classification_with_CoAtNet_and_ResNet18","url":"https://github.com/tyeso/Image_Classification_with_CoAtNet_and_ResNet18"},{"title":"mindspore-courses/External-Attention-MindSpore","url":"https://github.com/mindspore-courses/External-Attention-MindSpore/blob/main/model/attention/CoAtNet.py"},{"title":"LongLeCE/CoAtNet-PyTorch","url":"https://github.com/LongLeCE/CoAtNet-PyTorch"},{"title":"Burf/CoAtNet-Tensorflow2","url":"https://github.com/Burf/CoAtNet-Tensorflow2"},{"title":"MS-Mind/MS-Code-02","url":"https://github.com/MS-Mind/MS-Code-02/tree/main/configs/coat"},{"title":"pranavsinghps1/dedl","url":"https://github.com/pranavsinghps1/dedl"},{"title":"nqt228/CoAtNet-tensorflow","url":"https://github.com/nqt228/CoAtNet-tensorflow"},{"title":"hw666666666666/CoAtNet","url":"https://github.com/hw666666666666/CoAtNet"},{"title":"Mind23-2/MindCode-19","url":"https://github.com/Mind23-2/MindCode-19"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/coatnet-marrying-convolution-and-attention","title":"CoAtNet: Marrying Convolution and Attention for All Data Sizes","date":"2021-06-09","rows_on_this_dataset":1,"code_links":14,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":1,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/regnet-self-regulated-network-for-image","title":"RegNet: Self-Regulated Network for Image Classification","date":"2021-01-03","rows_on_this_dataset":1,"code_links":14,"syntology":null},{"paper":"/paper/efficientnet-rethinking-model-scaling-for","title":"EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks","date":"2019-05-28","rows_on_this_dataset":1,"code_links":144,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":302,"samples_ran":171,"samples_unverified":131,"pointer_only_for_licence":112,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/res2net-a-new-multi-scale-backbone","title":"Res2Net: A New Multi-scale Backbone Architecture","date":"2019-04-02","rows_on_this_dataset":1,"code_links":34,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":3,"samples_unverified":6,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/aggregated-residual-transformations-for-deep","title":"Aggregated Residual Transformations for Deep Neural Networks","date":"2016-11-16","rows_on_this_dataset":1,"code_links":61,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":80,"samples_ran":34,"samples_unverified":46,"pointer_only_for_licence":13,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","rows_on_this_dataset":1,"code_links":146,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":71,"samples_ran":18,"samples_unverified":53,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/deep-residual-learning-for-image-recognition","title":"Deep Residual Learning for Image Recognition","date":"2015-12-10","rows_on_this_dataset":2,"code_links":484,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":377,"samples_ran":230,"samples_unverified":147,"pointer_only_for_licence":187,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":6,"samples_harvested":844,"samples_ran":458,"samples_unverified":386,"pointer_only_for_licence":329,"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."}