{"about":{"non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","site":"https://codewithpapers.app","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page","syntology":{"site":"https://syntology.ai","developers":"https://syntology.ai/developers","mcp":{"server":"https://syntology.ai/mcp","transport":"streamable-http","server_card":"https://syntology.ai/.well-known/mcp/server-card.json","auth":{"type":"trial token, no account","trial_token":"https://syntology.ai/api/oauth/trial/token","method":"POST","docs":"https://syntology.ai/developers"}},"have":"https://syntology.ai/api/graph/have?x=<method, arXiv id or title> (free, answers coverage only)","paper_base":"https://syntology.ai/paper/","atlas_base":"https://app.syntology.ai/?focus="},"machine_readable":[{"url":"https://codewithpapers.app/llms.txt","what":"the machine catalog: every machine-readable file, counted"},{"url":"https://codewithpapers.app/index/manifest.json","what":"paper-to-code index by arXiv id, with Syntology's counts"},{"url":"https://codewithpapers.app/search/manifest.json","what":"site search index (titles, authors) and its files"},{"url":"https://codewithpapers.app/download","what":"bulk files: Syntology's layer, described there"},{"url":"https://codewithpapers.app/build_manifest.json","what":"the build record: inputs, counts, exclusions, probes"}]},"url":"/dataset/cvc-clinicdb/papers/ran/1","list_of":"/dataset/cvc-clinicdb","dataset":"CVC-ClinicDB","archive":{"snapshot":"2025-07-28"},"syntology_read_at":"2026-09-28T10:30:06+00:00","key_notes":{"samples_ran_checked":"legacy name, kept unchanged so existing readers do not break: it counts the samples that ran with no instrument failure (honoured, violated, and ran with no contract checked); it does not mean a contract was checked, and the pages print it as 'K with no instrument failure', not 'K checked'","samples_constructed":"a sub-count of the samples that ran, never subtracted from them and never a failure: an executed sample whose run returned an instance of its own class (fixture_out_type equals the entry name): the run built an object and did not compute a result (Syntology's RAN record, counts.constructed)"},"order":"ran","order_definition":"only papers where Syntology ran at least one harvested sample; date (newest first), ties by arXiv id","caption":"We ran code from the paper's repository; we did not run it on this dataset or check it against this dataset's benchmarks.","absence":"A paper missing from this list is not a recorded non-run: it may have no arXiv id, no harvested code, or only samples that have not run yet.","population":"every paper with a leaderboard row on this dataset's benchmarks (the benchmark-backed subset): the archive's own papers-using-this-dataset list was never published, so this is not that list; num_papers_in_archive is the archive's own count","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,8],"of":8,"counts":{"papers_with_a_benchmark_row":43,"with_a_code_link":38,"where_syntology_ran_a_sample":8,"not_listed_spam_title":0,"listed":43,"listed_where_code_ran":8,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":7,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":7,"listed_every_run_a_failure_of_syntologys_instrument":1,"filter":{"states":["a run with no instrument failure","any run, instrument failures included"],"default":"a run with no instrument failure","note":"on the 'only where code ran' pages the default hides, in the browser, the rows where every run was a failure of Syntology's instrument; the second state shows them again. Rows are hidden, never re-ordered; these twins list every row"}},"definition":"distinct papers with at least one leaderboard row on this dataset's benchmarks; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/dataset/cvc-clinicdb/papers/ran/1","prev":null,"next":null,"papers":[{"paper":"/paper/emcad-efficient-multi-scale-convolutional","slug":"emcad-efficient-multi-scale-convolutional","title":"EMCAD: Efficient Multi-scale Convolutional Attention Decoding for Medical Image Segmentation","date":"2024-05-11","arxiv_id":"2405.06880","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":23,"samples_ran":20,"samples_constructed":5,"samples_ran_checked":17,"samples_ran_instrument_failed":3,"samples_unverified":3,"pointer_only_for_licence":23,"official":{"repos":["sldgroup/emcad"],"state":"official (archive's flag): 20 ran","n_ran":20,"n_constructed":5,"n_ran_no_instrument_failure":17,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/emcad-efficient-multi-scale-convolutional#ran","syntology_url":"https://syntology.ai/paper/2405.06880","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2405.06880"}}}},{"paper":"/paper/g-cascade-efficient-cascaded-graph","slug":"g-cascade-efficient-cascaded-graph","title":"G-CASCADE: Efficient Cascaded Graph Convolutional Decoding for 2D Medical Image Segmentation","date":"2023-10-24","arxiv_id":"2310.16175","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":1,"samples_ran_instrument_failed":5,"samples_unverified":3,"pointer_only_for_licence":9,"official":{"repos":["SLDGroup/G-CASCADE"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/g-cascade-efficient-cascaded-graph#ran","syntology_url":"https://syntology.ai/paper/2310.16175","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2310.16175"}}}},{"paper":"/paper/uacanet-uncertainty-augmented-context","slug":"uacanet-uncertainty-augmented-context","title":"UACANet: Uncertainty Augmented Context Attention for Polyp Segmentation","date":"2021-07-06","arxiv_id":"2107.02368","rows_on_this_dataset":2,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":3,"pointer_only_for_licence":1,"official":{"repos":["plemeri/UACANet"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":3,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/uacanet-uncertainty-augmented-context#ran","syntology_url":"https://syntology.ai/paper/2107.02368","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02368"}}}},{"paper":"/paper/pranet-parallel-reverse-attention-network-for","slug":"pranet-parallel-reverse-attention-network-for","title":"PraNet: Parallel Reverse Attention Network for Polyp Segmentation","date":"2020-06-13","arxiv_id":"2006.11392","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":8,"samples_constructed":0,"samples_ran_checked":8,"samples_ran_instrument_failed":0,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["DengPingFan/PraNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/pranet-parallel-reverse-attention-network-for#ran","syntology_url":"https://syntology.ai/paper/2006.11392","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.11392"}}}},{"paper":"/paper/doubleu-net-a-deep-convolutional-neural","slug":"doubleu-net-a-deep-convolutional-neural","title":"DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation","date":"2020-06-08","arxiv_id":"2006.04868","rows_on_this_dataset":1,"code_links":4,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":5,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":3,"pointer_only_for_licence":5,"official":{"repos":["DebeshJha/2020-CBMS-DoubleU-Net"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["listed","official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/doubleu-net-a-deep-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/2006.04868","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2006.04868"}}}},{"paper":"/paper/resunet-an-advanced-architecture-for-medical","slug":"resunet-an-advanced-architecture-for-medical","title":"ResUNet++: An Advanced Architecture for Medical Image Segmentation","date":"2019-11-16","arxiv_id":"1911.07067","rows_on_this_dataset":1,"code_links":6,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":4,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":0,"samples_unverified":2,"pointer_only_for_licence":4,"official":{"repos":["DebeshJha/ResUNetplusplus"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/resunet-an-advanced-architecture-for-medical#ran","syntology_url":"https://syntology.ai/paper/1911.07067","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1911.07067"}}}},{"paper":"/paper/unet-a-nested-u-net-architecture-for-medical","slug":"unet-a-nested-u-net-architecture-for-medical","title":"UNet++: A Nested U-Net Architecture for Medical Image Segmentation","date":"2018-07-18","arxiv_id":"1807.10165","rows_on_this_dataset":1,"code_links":34,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":28,"samples_ran":21,"samples_constructed":0,"samples_ran_checked":18,"samples_ran_instrument_failed":3,"samples_unverified":7,"pointer_only_for_licence":3,"official":{"repos":["MrGiovanni/Nested-UNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/unet-a-nested-u-net-architecture-for-medical#ran","syntology_url":"https://syntology.ai/paper/1807.10165","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1807.10165"}}}},{"paper":"/paper/u-net-convolutional-networks-for-biomedical","slug":"u-net-convolutional-networks-for-biomedical","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation","date":"2015-05-18","arxiv_id":"1505.04597","rows_on_this_dataset":1,"code_links":487,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":757,"samples_ran":530,"samples_constructed":247,"samples_ran_checked":383,"samples_ran_instrument_failed":147,"samples_unverified":227,"pointer_only_for_licence":426,"official":null,"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/u-net-convolutional-networks-for-biomedical#ran","syntology_url":"https://syntology.ai/paper/1505.04597","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1505.04597"}}}}],"record_sha256":"4c42daac7e482681ce9ea709bb5f0c369fd7add6c0914fa72f1b0ece1bd2a2d9","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}