{"url":"/dataset/cvc-clinicdb","name":"CVC-ClinicDB","full_name":null,"description_markdown":"**CVC-ClinicDB** is an open-access dataset of 612 images with a resolution of 384×288 from 31 colonoscopy sequences.It is used for medical image segmentation, in particular polyp detection in colonoscopy videos.\r\n\r\nSource: [ResUNet++: An Advanced Architecture for Medical Image Segmentation](https://arxiv.org/abs/1911.07067)\r\nImage Source: [https://polyp.grand-challenge.org/CVCClinicDB/](https://polyp.grand-challenge.org/CVCClinicDB/)","description_withheld":null,"homepage":"https://polyp.grand-challenge.org/CVCClinicDB/","introduced_date":null,"introduced_date_note":null,"introduced_by":null,"license":null,"modalities":[{"name":"Images","url":"/datasets/modality/images"},{"name":"Medical","url":"/datasets/modality/medical"}],"tasks":[{"name":"Medical Image Segmentation","url":"/task/medical-image-segmentation","datasets_with_task":"/datasets/task/medical-image-segmentation"}],"languages":[{"name":"Chinese","url":"/datasets/language/chinese"}],"variants":["CVC-ClinicDB"],"data_loaders":[],"num_papers_in_archive":54,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/medical-image-segmentation-on-cvc-clinicdb","task":"Medical Image Segmentation","dataset_variant":"CVC-ClinicDB","rows":48,"metrics":["mean Dice","Average MAE","S-Measure","mIoU","max E-Measure","F-measure"],"first_row_in_archive_order":{"model":"DUCK-Net","paper":"/paper/adaptive-t-vmf-dice-loss-for-multi-class","metrics":{"mIoU":"0.9343","mean Dice":"0.9684"},"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"}],"papers_with_a_benchmark_row":[{"paper":"/paper/uninet-a-contrastive-learning-guided-unified","title":"UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection","date":"2025-02-28","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/from-semantic-segmentation-of-natural-images","title":"From Semantic Segmentation of Natural Images to Medical Image Segmentation Using ViT-Based Architectures","date":"2025-01-31","rows_on_this_dataset":1,"code_links":0,"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/metaformer-and-cnn-hybrid-model-for-polyp","title":"MetaFormer and CNN Hybrid Model for Polyp Image Segmentation","date":"2024-09-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/self-prompting-polyp-segmentation-in","title":"Self-Prompting Polyp Segmentation in Colonoscopy using Hybrid Yolo-SAM 2 Model","date":"2024-09-14","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/sam-eg-segment-anything-model-with-egde","title":"SAM-EG: Segment Anything Model with Egde Guidance framework for efficient Polyp Segmentation","date":"2024-06-21","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/hi-gmisnet-generalized-medical-image","title":"Hi-gMISnet: generalized medical image segmentation using DWT based multilayer fusion and dual mode attention into high resolution pGAN","date":"2024-05-20","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/adaptation-of-distinct-semantics-for","title":"Adaptation of Distinct Semantics for Uncertain Areas in Polyp Segmentation","date":"2024-05-13","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":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/modality-agnostic-domain-generalizable","title":"Modality-agnostic Domain Generalizable Medical Image Segmentation by Multi-Frequency in Multi-Scale Attention","date":"2024-05-10","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/kdas3-knowledge-distillation-via-attention","title":"KDAS: Knowledge Distillation via Attention Supervision Framework for Polyp Segmentation","date":"2023-12-13","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/using-duck-net-for-polyp-image-segmentation-1","title":"Using DUCK-Net for Polyp Image Segmentation","date":"2023-11-03","rows_on_this_dataset":1,"code_links":2,"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":1,"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/m3fpolypsegnet-segmentation-network-with","title":"M3FPolypSegNet: Segmentation Network with Multi-frequency Feature Fusion for Polyp Localization in Colonoscopy Images","date":"2023-10-09","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/meganet-multi-scale-edge-guided-attention","title":"MEGANet: Multi-Scale Edge-Guided Attention Network for Weak Boundary Polyp Segmentation","date":"2023-09-06","rows_on_this_dataset":2,"code_links":2,"syntology":null},{"paper":"/paper/polyp-sam-can-a-text-guided-sam-perform","title":"Polyp-SAM++: Can A Text Guided SAM Perform Better for Polyp Segmentation?","date":"2023-08-12","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/ugcanet-a-unified-global-context-aware","title":"UGCANet: A Unified Global Context-Aware Transformer-based Network with Feature Alignment for Endoscopic Image Analysis","date":"2023-07-12","rows_on_this_dataset":1,"code_links":0,"syntology":null},{"paper":"/paper/rabit-an-efficient-transformer-using","title":"RaBiT: An Efficient Transformer using Bidirectional Feature Pyramid Network with Reverse Attention for Colon Polyp Segmentation","date":"2023-07-12","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/dual-cross-attention-for-medical-image","title":"Dual Cross-Attention for Medical Image Segmentation","date":"2023-03-30","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":1,"code_links":1,"syntology":null},{"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/hardnet-dfus-an-enhanced-harmonically","title":"HarDNet-DFUS: An Enhanced Harmonically-Connected Network for Diabetic Foot Ulcer Image Segmentation and Colonoscopy Polyp Segmentation","date":"2022-09-15","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"paper":"/paper/fcn-transformer-feature-fusion-for-polyp","title":"FCN-Transformer Feature Fusion for Polyp Segmentation","date":"2022-08-17","rows_on_this_dataset":1,"code_links":2,"syntology":null},{"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/esfpnet-efficient-deep-learning-architecture","title":"ESFPNet: efficient deep learning architecture for real-time lesion segmentation in autofluorescence bronchoscopic video","date":"2022-07-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/colonformer-an-efficient-transformer-based","title":"ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentation","date":"2022-05-17","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/comma-propagating-complementary-multi-level","title":"COMMA: Propagating Complementary Multi-Level Aggregation Network for Polyp Segmentation","date":"2022-02-17","rows_on_this_dataset":2,"code_links":0,"syntology":null},{"paper":"/paper/training-on-polar-image-transformations","title":"Training on Polar Image Transformations Improves Biomedical Image Segmentation","date":"2021-09-29","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/caranet-context-axial-reverse-attention","title":"CaraNet: Context Axial Reverse Attention Network for Segmentation of Small Medical Objects","date":"2021-08-16","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/a-comprehensive-study-on-colorectal-polyp","title":"A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation","date":"2021-07-26","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/uacanet-uncertainty-augmented-context","title":"UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation","date":"2021-07-06","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":1,"samples_unverified":3,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/ag-curesnest-a-novel-method-for-colon-polyp","title":"AG-CUResNeSt: A Novel Method for Colon Polyp Segmentation","date":"2021-05-02","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/transfuse-fusing-transformers-and-cnns-for","title":"TransFuse: Fusing Transformers and CNNs for Medical Image Segmentation","date":"2021-02-16","rows_on_this_dataset":2,"code_links":1,"syntology":null},{"paper":"/paper/hardnet-mseg-a-simple-encoder-decoder-polyp","title":"HarDNet-MSEG: A Simple Encoder-Decoder Polyp Segmentation Neural Network that Achieves over 0.9 Mean Dice and 86 FPS","date":"2021-01-18","rows_on_this_dataset":1,"code_links":4,"syntology":null},{"paper":"/paper/real-time-polyp-detection-localisation-and","title":"Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning","date":"2020-11-15","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/pranet-parallel-reverse-attention-network-for","title":"PraNet: Parallel Reverse Attention Network for Polyp Segmentation","date":"2020-06-13","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":9,"samples_ran":0,"samples_unverified":9,"pointer_only_for_licence":0,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim."}},{"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/resunet-an-advanced-architecture-for-medical","title":"ResUNet++: An Advanced Architecture for Medical Image Segmentation","date":"2019-11-16","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":4,"samples_ran":2,"samples_unverified":2,"pointer_only_for_licence":4,"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."}},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-24T18:15:14+00:00","samples_harvested":757,"samples_ran":510,"samples_unverified":247,"pointer_only_for_licence":426,"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":8,"samples_harvested":839,"samples_ran":546,"samples_unverified":293,"pointer_only_for_licence":469,"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."}