{"url":"/dataset/acdc-scribbles","name":"ACDC Scribbles","full_name":null,"description_markdown":"We release expert-made scribble annotations for the medical ACDC dataset [1]. The released data must be considered as extending the original ACDC dataset.\r\nThe ACDC dataset contains cardiac MRI images, paired with hand-made segmentation masks. It is possible to use the segmentation masks provided in the ACDC dataset to evaluate the performance of methods trained using only scribble supervision. \r\n\r\nReferences: \r\n[1] Bernard, Olivier, et al. \"Deep learning techniques for automatic MRI cardiac multi-structures segmentation and diagnosis: is the problem solved?.\" IEEE transactions on medical imaging 37.11 (2018): 2514-2525.","description_withheld":null,"homepage":"https://vios-s.github.io/multiscale-adversarial-attention-gates","introduced_date":"2020-07-02","introduced_date_note":null,"introduced_by":{"paper":"/paper/weakly-supervised-segmentation-with-multi","title":"Learning to Segment from Scribbles using Multi-scale Adversarial Attention Gates","first_author":"Gabriele Valvano","url":null},"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"},{"name":"MRI","url":"/datasets/modality/mri"}],"tasks":[{"name":"Semantic Segmentation","url":"/task/semantic-segmentation","datasets_with_task":"/datasets/task/semantic-segmentation"},{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Weakly-Supervised Semantic Segmentation","url":"/task/weakly-supervised-semantic-segmentation","datasets_with_task":"/datasets/task/weakly-supervised-semantic-segmentation"},{"name":"Heart Segmentation","url":"/task/heart-segmentation","datasets_with_task":"/datasets/task/heart-segmentation"},{"name":"Cardiac Segmentation","url":"/task/cardiac-segmentation","datasets_with_task":"/datasets/task/cardiac-segmentation"},{"name":"Weakly supervised segmentation","url":"/task/weakly-supervised-segmentation","datasets_with_task":"/datasets/task/weakly-supervised-segmentation"}],"languages":[],"variants":["ACDC Scribbles"],"data_loaders":[],"num_papers_in_archive":10,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/semantic-segmentation-on-acdc-scribbles","task":"Semantic Segmentation","dataset_variant":"ACDC Scribbles","rows":6,"metrics":["Dice (Average)"],"first_row_in_archive_order":{"model":"ScribFormer","paper":"/paper/scribformer-transformer-makes-cnn-work-better","metrics":{"Dice (Average)":"88.8%"},"code_links":[{"title":"HUANGLIZI/ScribFormer","url":"https://github.com/HUANGLIZI/ScribFormer"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/scribformer-transformer-makes-cnn-work-better","title":"ScribFormer: Transformer Makes CNN Work Better for Scribble-based Medical Image Segmentation","date":"2024-02-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/scribblevc-scribble-supervised-medical-image","title":"ScribbleVC: Scribble-supervised Medical Image Segmentation with Vision-Class Embedding","date":"2023-07-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/tfcns-a-cnn-transformer-hybrid-network-for","title":"TFCNs: A CNN-Transformer Hybrid Network for Medical Image Segmentation","date":"2022-07-07","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/cyclemix-a-holistic-strategy-for-medical","title":"CycleMix: A Holistic Strategy for Medical Image Segmentation from Scribble Supervision","date":"2022-03-03","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/puzzle-mix-exploiting-saliency-and-local-1","title":"Puzzle Mix: Exploiting Saliency and Local Statistics for Optimal Mixup","date":"2020-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":0,"samples_unverified":1,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/cutmix-regularization-strategy-to-train","title":"CutMix: Regularization Strategy to Train Strong Classifiers with Localizable Features","date":"2019-05-13","rows_on_this_dataset":1,"code_links":30,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":24,"samples_ran":17,"samples_unverified":7,"pointer_only_for_licence":5,"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":2,"samples_harvested":25,"samples_ran":17,"samples_unverified":8,"pointer_only_for_licence":5,"papers_with_no_sample_that_ran":1,"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."}