{"url":"/dataset/acdc","name":"ACDC","full_name":"Automated Cardiac Diagnosis Challenge","description_markdown":"The goal of the **Automated Cardiac Diagnosis Challenge (ACDC)** challenge is to:\r\n\r\n- compare the performance of automatic methods on the segmentation of the left ventricular endocardium and epicardium as the right ventricular endocardium for both end diastolic and end systolic phase instances;\r\n- compare the performance of automatic methods for the classification of the examinations in five classes (normal case, heart failure with infarction, dilated cardiomyopathy, hypertrophic cardiomyopathy, abnormal right ventricle).\r\n\r\nThe overall **ACDC** dataset was created from real clinical exams acquired at the University Hospital of Dijon. Acquired data were fully anonymized and handled within the regulations set by the local ethical committee of the Hospital of Dijon (France). Our dataset covers several well-defined pathologies with enough cases to (1) properly train machine learning methods and (2) clearly assess the variations of the main physiological parameters obtained from cine-MRI (in particular diastolic volume and ejection fraction). The dataset is composed of 150 exams (all from different patients) divided into 5 evenly distributed subgroups (4 pathological plus 1 healthy subject groups) as described below. Furthermore, each patient comes with the following additional information : weight, height, as well as the diastolic and systolic phase instants.\r\n\r\nThe database is made available to participants through two datasets from the dedicated online evaluation website after a personal registration: i) a training dataset of 100 patients along with the corresponding manual references based on the analysis of one clinical expert; ii) a testing dataset composed of 50 new patients, without manual annotations but with the patient information given above. The raw input images are provided through the Nifti format.\r\n\r\nSource: [Automated Cardiac Diagnosis Challenge](https://acdc.creatis.insa-lyon.fr/description/databases.html)\r\n\r\nImage source: [Automated Cardiac Diagnosis Challenge](https://acdc.creatis.insa-lyon.fr/description/databases.html)","description_withheld":null,"homepage":"https://acdc.creatis.insa-lyon.fr/description/databases.html","introduced_date":"2021-09-15","introduced_date_note":null,"introduced_by":null,"license":{"name":"CC BY-NC-SA 4.0","url":"https://humanheart-project.creatis.insa-lyon.fr/database/#item/66e290e0961576b1bad4ee3c"},"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Biomedical","url":"/datasets/modality/biomedical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"},{"name":"Semi-supervised Medical Image Segmentation","url":"/task/semi-supervised-medical-image-segmentation","datasets_with_task":"/datasets/task/semi-supervised-medical-image-segmentation"},{"name":"Medical Image Generation","url":"/task/medical-image-generation","datasets_with_task":"/datasets/task/medical-image-generation"},{"name":"Diffeomorphic Medical Image Registration","url":"/task/diffeomorphic-medical-image-registration","datasets_with_task":"/datasets/task/diffeomorphic-medical-image-registration"}],"languages":[],"variants":["Automatic Cardiac Diagnosis Challenge (ACDC)","ACDC","ACDC 20% labeled data"],"data_loaders":[{"repo":"https://github.com/ycwu1997/ss-net","url":"https://github.com/ycwu1997/ss-net","frameworks":["pytorch"]}],"num_papers_in_archive":52,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-automatic","task":"Medical Image Segmentation","dataset_variant":"Automatic Cardiac Diagnosis Challenge (ACDC)","rows":20,"metrics":["Avg DSC"],"first_row_in_archive_order":{"model":"FCT","paper":"/paper/adaptive-t-vmf-dice-loss-for-multi-class","metrics":{"Avg DSC":"94.26"},"code_links":[{"title":"usagisukisuki/adaptive_t-vmf_dice_loss","url":"https://github.com/usagisukisuki/adaptive_t-vmf_dice_loss"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-segmentation-on-acdc","task":"Medical Image Segmentation","dataset_variant":"ACDC","rows":6,"metrics":["Dice Score"],"first_row_in_archive_order":{"model":"FCT","paper":"/paper/the-fully-convolutional-transformer-for","metrics":{"Dice Score":"0.9302"},"code_links":[{"title":"thanos-db/fullyconvolutionaltransformer","url":"https://github.com/thanos-db/fullyconvolutionaltransformer"},{"title":"kingo233/FCT-Pytorch","url":"https://github.com/kingo233/FCT-Pytorch"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/semi-supervised-medical-image-segmentation-on-2","task":"Semi-supervised Medical Image Segmentation","dataset_variant":"ACDC 20% labeled data","rows":4,"metrics":["Dice (Average)"],"first_row_in_archive_order":{"model":"PatchCL","paper":"/paper/pseudo-label-guided-contrastive-learning-for","metrics":{"Dice (Average)":"91.20"},"code_links":[{"title":"HiLab-git/SSL4MIS","url":"https://github.com/HiLab-git/SSL4MIS"},{"title":"hritam-98/patchcl-medseg","url":"https://github.com/hritam-98/patchcl-medseg"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/diffeomorphic-medical-image-registration-on-1","task":"Diffeomorphic Medical Image Registration","dataset_variant":"Automatic Cardiac Diagnosis Challenge (ACDC)","rows":3,"metrics":["Dice","RMSE","Hausdorff Distance (mm)","Grad Det-Jac"],"first_row_in_archive_order":{"model":"cVAE Diffeomorphic (S3)","paper":"/paper/learning-a-probabilistic-model-for","metrics":{"Dice":"0.812","Grad Det-Jac":"1.4","Hausdorff Distance (mm)":"7.3","RMSE":"0.30"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"},{"leaderboard":"/sota/medical-image-generation-on-acdc","task":"Medical Image Generation","dataset_variant":"ACDC","rows":3,"metrics":["FID"],"first_row_in_archive_order":{"model":"StyleGAN2-ADA","paper":"/paper/evaluating-the-performance-of-stylegan2-ada","metrics":{"FID":"21.17"},"code_links":[]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/rwkv-unet-improving-unet-with-long-range","title":"RWKV-UNet: Improving UNet with Long-Range Cooperation for Effective Medical Image Segmentation","date":"2025-01-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/s2s2-semantic-stacking-for-robust-semantic","title":"S2S2: Semantic Stacking for Robust Semantic Segmentation in Medical Imaging","date":"2024-12-17","rows_on_this_dataset":1,"code_links":1,"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":2,"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/segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":2,"samples_ran":2,"samples_unverified":0,"pointer_only_for_licence":2,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/lhu-net-a-light-hybrid-u-net-for-cost","title":"LHU-Net: A Light Hybrid U-Net for Cost-Efficient, High-Performance Volumetric Medical Image Segmentation","date":"2024-04-07","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":3,"samples_ran":3,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/agileformer-spatially-agile-transformer-unet","title":"AgileFormer: Spatially Agile Transformer UNet for Medical Image Segmentation","date":"2024-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/ai-sam-automatic-and-interactive-segment","title":"AI-SAM: Automatic and Interactive Segment Anything Model","date":"2023-12-05","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/importance-of-feature-extraction-in-the","title":"Feature Extraction for Generative Medical Imaging Evaluation: New Evidence Against an Evolving Trend","date":"2023-11-22","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/mist-medical-image-segmentation-transformer","title":"MIST: Medical Image Segmentation Transformer with Convolutional Attention Mixing (CAM) Decoder","date":"2023-10-30","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/g-cascade-efficient-cascaded-graph","title":"G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation","date":"2023-10-24","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":6,"samples_unverified":3,"pointer_only_for_licence":9,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/bidirectional-copy-paste-for-semi-supervised","title":"Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation","date":"2023-05-01","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":19,"samples_ran":6,"samples_unverified":13,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/multi-scale-hierarchical-vision-transformer-1","title":"Multi-scale Hierarchical Vision Transformer with Cascaded Attention Decoding for Medical Image Segmentation","date":"2023-03-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/medical-image-segmentation-via-cascaded","title":"Medical Image Segmentation via Cascaded Attention Decoding","date":"2023-01-03","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/pseudo-label-guided-contrastive-learning-for","title":"Pseudo-Label Guided Contrastive Learning for Semi-Supervised Medical Image Segmentation","date":"2023-01-01","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/caussl-causality-inspired-semi-supervised","title":"CauSSL: Causality-inspired Semi-supervised Learning for Medical Image Segmentation","date":"2023-01-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/evaluating-the-performance-of-stylegan2-ada","title":"Evaluating the Performance of StyleGAN2-ADA on Medical Images","date":"2022-10-07","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/revisiting-weak-to-strong-consistency-in-semi","title":"Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation","date":"2022-08-21","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":4,"samples_unverified":5,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/adaptive-t-vmf-dice-loss-for-multi-class","title":"Adaptive t-vMF Dice Loss for Multi-class Medical Image Segmentation","date":"2022-07-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/the-fully-convolutional-transformer-for","title":"The Fully Convolutional Transformer for Medical Image Segmentation","date":"2022-06-01","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/missformer-an-effective-medical-image","title":"MISSFormer: An Effective Medical Image Segmentation Transformer","date":"2021-09-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/nnformer-interleaved-transformer-for","title":"nnFormer: Interleaved Transformer for Volumetric Segmentation","date":"2021-09-07","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":1,"samples_ran":1,"samples_unverified":0,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/swin-unet-unet-like-pure-transformer-for","title":"Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation","date":"2021-05-12","rows_on_this_dataset":2,"code_links":7,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":18,"samples_ran":3,"samples_unverified":15,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/gans-for-medical-image-synthesis-an-empirical","title":"GANs for Medical Image Synthesis: An Empirical Study","date":"2021-05-11","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/transunet-transformers-make-strong-encoders","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","date":"2021-02-08","rows_on_this_dataset":4,"code_links":22,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":7,"samples_ran":6,"samples_unverified":1,"pointer_only_for_licence":7,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"paper":"/paper/learning-a-probabilistic-model-for","title":"Learning a Probabilistic Model for Diffeomorphic Registration","date":"2018-12-18","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/voxelmorph-a-learning-framework-for","title":"VoxelMorph: A Learning Framework for Deformable Medical Image Registration","date":"2018-09-14","rows_on_this_dataset":1,"code_links":9,"syntology":null},{"paper":"/paper/symmetric-diffeomorphic-image-registration","title":"Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain","date":"2008-02-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":9,"samples_harvested":91,"samples_ran":51,"samples_unverified":40,"pointer_only_for_licence":41,"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."}