{"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/acdc/papers/ran/1","list_of":"/dataset/acdc","dataset":"ACDC","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":[1,9],"of":9,"counts":{"papers_with_a_benchmark_row":27,"with_a_code_link":24,"where_syntology_ran_a_sample":9,"not_listed_spam_title":0,"listed":27,"listed_where_code_ran":9,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":7,"every_run_a_failure_of_syntologys_instrument":2,"listed_with_a_run_with_no_instrument_failure":7,"listed_every_run_a_failure_of_syntologys_instrument":2,"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/acdc/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":2,"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/segformer3d-an-efficient-transformer-for-3d","slug":"segformer3d-an-efficient-transformer-for-3d","title":"SegFormer3D: an Efficient Transformer for 3D Medical Image Segmentation","date":"2024-04-15","arxiv_id":"2404.10156","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":2,"samples_ran":2,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":2,"samples_unverified":0,"pointer_only_for_licence":2,"official":{"repos":["osupcvlab/segformer3d"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/segformer3d-an-efficient-transformer-for-3d#ran","syntology_url":"https://syntology.ai/paper/2404.10156","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.10156"}}}},{"paper":"/paper/lhu-net-a-light-hybrid-u-net-for-cost","slug":"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","arxiv_id":"2404.05102","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":3,"samples_ran":3,"samples_constructed":0,"samples_ran_checked":3,"samples_ran_instrument_failed":0,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["xmindflow/lhunet"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/lhu-net-a-light-hybrid-u-net-for-cost#ran","syntology_url":"https://syntology.ai/paper/2404.05102","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2404.05102"}}}},{"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":2,"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/bidirectional-copy-paste-for-semi-supervised","slug":"bidirectional-copy-paste-for-semi-supervised","title":"Bidirectional Copy-Paste for Semi-Supervised Medical Image Segmentation","date":"2023-05-01","arxiv_id":"2305.00673","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":19,"samples_ran":17,"samples_constructed":2,"samples_ran_checked":15,"samples_ran_instrument_failed":2,"samples_unverified":2,"pointer_only_for_licence":0,"official":{"repos":["HiLab-git/SSL4MIS","deepmed-lab-ecnu/bcp"],"state":"official (archive's flag): 17 ran","n_ran":17,"n_constructed":2,"n_ran_no_instrument_failure":15,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/bidirectional-copy-paste-for-semi-supervised#ran","syntology_url":"https://syntology.ai/paper/2305.00673","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00673"}}}},{"paper":"/paper/revisiting-weak-to-strong-consistency-in-semi","slug":"revisiting-weak-to-strong-consistency-in-semi","title":"Revisiting Weak-to-Strong Consistency in Semi-Supervised Semantic Segmentation","date":"2022-08-21","arxiv_id":"2208.09910","rows_on_this_dataset":1,"code_links":1,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":9,"samples_ran":7,"samples_constructed":0,"samples_ran_checked":4,"samples_ran_instrument_failed":3,"samples_unverified":2,"pointer_only_for_licence":6,"official":{"repos":["LiheYoung/UniMatch"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/revisiting-weak-to-strong-consistency-in-semi#ran","syntology_url":"https://syntology.ai/paper/2208.09910","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2208.09910"}}}},{"paper":"/paper/nnformer-interleaved-transformer-for","slug":"nnformer-interleaved-transformer-for","title":"nnFormer: Interleaved Transformer for Volumetric Segmentation","date":"2021-09-07","arxiv_id":"2109.03201","rows_on_this_dataset":1,"code_links":2,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":1,"samples_ran":1,"samples_constructed":0,"samples_ran_checked":0,"samples_ran_instrument_failed":1,"samples_unverified":0,"pointer_only_for_licence":0,"official":{"repos":["282857341/nnformer"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["official"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/nnformer-interleaved-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2109.03201","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2109.03201"}}}},{"paper":"/paper/swin-unet-unet-like-pure-transformer-for","slug":"swin-unet-unet-like-pure-transformer-for","title":"Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation","date":"2021-05-12","arxiv_id":"2105.05537","rows_on_this_dataset":2,"code_links":7,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":18,"samples_ran":17,"samples_constructed":0,"samples_ran_checked":14,"samples_ran_instrument_failed":3,"samples_unverified":1,"pointer_only_for_licence":0,"official":{"repos":["HuCaoFighting/Swin-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/swin-unet-unet-like-pure-transformer-for#ran","syntology_url":"https://syntology.ai/paper/2105.05537","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.05537"}}}},{"paper":"/paper/transunet-transformers-make-strong-encoders","slug":"transunet-transformers-make-strong-encoders","title":"TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation","date":"2021-02-08","arxiv_id":"2102.04306","rows_on_this_dataset":4,"code_links":22,"syntology":{"read_at":"2026-09-28T10:30:06+00:00","samples_harvested":7,"samples_ran":6,"samples_constructed":0,"samples_ran_checked":2,"samples_ran_instrument_failed":4,"samples_unverified":1,"pointer_only_for_licence":7,"official":{"repos":["Beckschen/TransUNet"],"state":"official: no sample here; runs from other or unrecorded repositories","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed","unlocated"]},"claim":"Per-sample execution on synthesized fixtures; not a correctness claim.","sample_list":"/paper/transunet-transformers-make-strong-encoders#ran","syntology_url":"https://syntology.ai/paper/2102.04306","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2102.04306"}}}}],"record_sha256":"43a19fc8631198960f06cb9d03567e55f6f056081fd9174267fa0cb33038295a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}