{"url":"/dataset/2018-data-science-bowl","name":"2018 Data Science Bowl","full_name":"2018 Data Science Bowl Find the nuclei in divergent images to advance medical discovery","description_markdown":"This dataset contains a large number of segmented nuclei images. The images were acquired under a variety of conditions and vary in the cell type, magnification, and imaging modality (brightfield vs. fluorescence). The dataset is designed to challenge an algorithm's ability to generalize across these variations.\r\n\r\nEach image is represented by an associated ImageId. Files belonging to an image are contained in a folder with this ImageId. Within this folder are two subfolders:\r\n\r\nimages contains the image file.\r\nmasks contains the segmented masks of each nucleus. This folder is only included in the training set. Each mask contains one nucleus. Masks are not allowed to overlap (no pixel belongs to two masks).\r\nThe second stage dataset will contain images from unseen experimental conditions. To deter hand labeling, it will also contain images that are ignored in scoring. The metric used to score this competition requires that your submissions are in run-length encoded format. Please see the evaluation page for details.\r\n\r\nAs with any human-annotated dataset, you may find various forms of errors in the data. You may manually correct errors you find in the training set. The dataset will not be updated/re-released unless it is determined that there are a large number of systematic errors. The masks of the stage 1 test set will be released with the release of the stage 2 test set.","description_withheld":null,"homepage":"https://www.kaggle.com/c/data-science-bowl-2018/overview","introduced_date":"2018-07-18","introduced_date_note":null,"introduced_by":{"paper":"/paper/unet-a-nested-u-net-architecture-for-medical","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","first_author":"Zongwei Zhou","url":null},"license":{"name":"https://www.kaggle.com/c/data-science-bowl-2018/overview","url":"https://www.kaggle.com/c/data-science-bowl-2018/overview"},"modalities":[{"name":"Images","url":"/datasets/modality/images"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Nuclear Segmentation","url":"/task/nuclear-segmentation","datasets_with_task":"/datasets/task/nuclear-segmentation"}],"languages":[{"name":"English","url":"/datasets/language/english"}],"variants":["2018 Data Science Bowl"],"data_loaders":[{"repo":"https://github.com/MrGiovanni/UNetPlusPlus","url":"https://github.com/MrGiovanni/UNetPlusPlus","frameworks":["pytorch"]}],"num_papers_in_archive":49,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-2018-data","task":"Medical Image Segmentation","dataset_variant":"2018 Data Science Bowl","rows":10,"metrics":["Dice","mIoU","Recall","Precision","AHD95","ASD"],"first_row_in_archive_order":{"model":"ReN-UNet","paper":"/paper/rethinking-the-nested-u-net-approach","metrics":{"AHD95":"6.5914","ASD":"1.7074","Dice":"92.79","mIoU":"87.22"},"code_links":[{"title":"saadwazir/ReN-UNet","url":"https://github.com/saadwazir/ReN-UNet"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rethinking-the-nested-u-net-approach","title":"Rethinking the Nested U-Net Approach: Enhancing Biomarker Segmentation with Attention Mechanisms and Multiscale Feature Fusion","date":"2025-04-08","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/trans2unet-neural-fusion-for-nuclei-semantic","title":"Trans2Unet: Neural fusion for Nuclei Semantic Segmentation","date":"2024-07-24","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":23,"samples_ran":20,"samples_unverified":3,"pointer_only_for_licence":23,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/duat-dual-aggregation-transformer-network-for","title":"DuAT: Dual-Aggregation Transformer Network for Medical Image Segmentation","date":"2022-12-21","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stepwise-feature-fusion-local-guides-global","title":"Stepwise Feature Fusion: Local Guides Global","date":"2022-03-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dcsau-net-a-deeper-and-more-compact-split","title":"DCSAU-Net: A Deeper and More Compact Split-Attention U-Net for Medical Image Segmentation","date":"2022-02-02","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/msrf-net-a-multi-scale-residual-fusion","title":"MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation","date":"2021-05-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/fanet-a-feedback-attention-network-for","title":"FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation","date":"2021-03-31","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/doubleu-net-a-deep-convolutional-neural","title":"DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation","date":"2020-06-08","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":5,"samples_ran":2,"samples_unverified":3,"pointer_only_for_licence":5,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/unet-a-nested-u-net-architecture-for-medical","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","date":"2018-07-18","rows_on_this_dataset":1,"code_links":34,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":28,"samples_ran":5,"samples_unverified":23,"pointer_only_for_licence":2,"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":3,"samples_harvested":56,"samples_ran":27,"samples_unverified":29,"pointer_only_for_licence":30,"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."}