{"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":"/task/retinal-vessel-segmentation/papers/ran/1","list_of":"/task/retinal-vessel-segmentation","task":"Retinal Vessel Segmentation","archive":{"snapshot":"2025-07-28"},"key_notes":{"n_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'","n_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)"},"syntology_read_at":"2026-09-28T10:30:06+00:00","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 task or check it against the task'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.","page":1,"pages_in_order":1,"rows_per_page":100,"rows":[1,5],"of":5,"counts":{"archive_papers_tagged":139,"with_a_code_link":59,"where_syntology_ran_a_sample":5,"not_listed_spam_title":0,"listed":139,"listed_where_code_ran":5,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":4,"every_run_a_failure_of_syntologys_instrument":1,"listed_with_a_run_with_no_instrument_failure":4,"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 the archive tags; 'where Syntology ran a sample' counts papers with at least one harvested sample that ran, which is not a correctness claim"},"first_page":"/task/retinal-vessel-segmentation/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/conformal-performance-range-prediction-for","slug":"conformal-performance-range-prediction-for","title":"Conformal Performance Range Prediction for Segmentation Output Quality Control","date":"2024-07-18","arxiv_id":"2407.13307","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":2,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 2 violated, 0 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/conformal-performance-range-prediction-for#ran","syntology_url":"https://syntology.ai/paper/2407.13307","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.13307"}},"official":{"repos":["annawundram/PerformanceRangePrediction"],"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"]}}},{"url":"/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","repositories_listed":1,"syntology":{"n":9,"n_ran":6,"n_constructed":0,"n_ran_checked":1,"n_instrument":5,"n_unverified":3,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":9,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 1 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","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"}},"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"]}}},{"url":"/paper/rose-a-retinal-oct-angiography-vessel","slug":"rose-a-retinal-oct-angiography-vessel","title":"ROSE: A Retinal OCT-Angiography Vessel Segmentation Dataset and New Model","date":"2020-07-10","arxiv_id":"2007.05201","repositories_listed":1,"syntology":{"n":11,"n_ran":10,"n_constructed":0,"n_ran_checked":8,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":8,"n_pointer_only":1,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 8 with no instrument failure: 0 honoured, 0 violated, 8 with no contract checked; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/rose-a-retinal-oct-angiography-vessel#ran","syntology_url":"https://syntology.ai/paper/2007.05201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2007.05201"}},"official":null}},{"url":"/paper/recurrent-residual-convolutional-neural","slug":"recurrent-residual-convolutional-neural","title":"Recurrent Residual Convolutional Neural Network based on U-Net (R2U-Net) for Medical Image Segmentation","date":"2018-02-20","arxiv_id":"1802.06955","repositories_listed":12,"syntology":{"n":8,"n_ran":5,"n_constructed":0,"n_ran_checked":0,"n_instrument":5,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":5,"phrase":"5 ran (of which 0 constructed an object rather than computing a result; 0 with no instrument failure: 0 honoured, 0 violated, 0 with no contract checked; 5 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/recurrent-residual-convolutional-neural#ran","syntology_url":"https://syntology.ai/paper/1802.06955","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1802.06955"}},"official":null}},{"url":"/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","repositories_listed":487,"syntology":{"n":757,"n_ran":530,"n_constructed":247,"n_ran_checked":383,"n_instrument":147,"n_unverified":227,"n_honours":14,"n_violates":6,"n_no_contract":363,"n_pointer_only":426,"phrase":"530 ran (of which 247 constructed an object rather than computing a result; 383 with no instrument failure: 14 honoured, 6 violated, 363 with no contract checked; 147 where Syntology's instrument failed) · 227 unverified","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"}},"official":null}}],"record_sha256":"0940d7ba170f71d30c03c2150c7ff6abc7ab55977b22e69594e788dadd783c41","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}