{"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/breast-tumour-classification/papers/ran/1","list_of":"/task/breast-tumour-classification","task":"Breast Tumour Classification","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":13,"with_a_code_link":10,"where_syntology_ran_a_sample":5,"not_listed_spam_title":0,"listed":13,"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/breast-tumour-classification/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/virchow-a-million-slide-digital-pathology","slug":"virchow-a-million-slide-digital-pathology","title":"Virchow: A Million-Slide Digital Pathology Foundation Model","date":"2023-09-14","arxiv_id":"2309.07778","repositories_listed":1,"syntology":{"n":9,"n_ran":9,"n_constructed":0,"n_ran_checked":9,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":9,"n_pointer_only":9,"phrase":"9 ran (of which 0 constructed an object rather than computing a result; 9 with no instrument failure: 0 honoured, 0 violated, 9 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/virchow-a-million-slide-digital-pathology#ran","syntology_url":"https://syntology.ai/paper/2309.07778","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07778"}},"official":{"repos":["Paige-AI/paige-ml-sdk"],"state":"official (archive's flag): 9 ran","n_ran":9,"n_constructed":0,"n_ran_no_instrument_failure":9,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/multi-view-breast-cancer-classification-via","slug":"multi-view-breast-cancer-classification-via","title":"Multi-View Hypercomplex Learning for Breast Cancer Screening","date":"2022-04-12","arxiv_id":"2204.05798","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":3,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 7 with no instrument failure: 0 honoured, 0 violated, 7 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/multi-view-breast-cancer-classification-via#ran","syntology_url":"https://syntology.ai/paper/2204.05798","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2204.05798"}},"official":{"repos":["ispamm/phbreast"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/dense-steerable-filter-cnns-for-exploiting","slug":"dense-steerable-filter-cnns-for-exploiting","title":"Dense Steerable Filter CNNs for Exploiting Rotational Symmetry in Histology Images","date":"2020-04-06","arxiv_id":"2004.03037","repositories_listed":2,"syntology":{"n":1,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"1 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/dense-steerable-filter-cnns-for-exploiting#ran","syntology_url":"https://syntology.ai/paper/2004.03037","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2004.03037"}},"official":{"repos":["simongraham/dsf-cnn"],"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"]}}},{"url":"/paper/roto-translation-equivariant-convolutional","slug":"roto-translation-equivariant-convolutional","title":"Roto-Translation Equivariant Convolutional Networks: Application to Histopathology Image Analysis","date":"2020-02-20","arxiv_id":"2002.08725","repositories_listed":1,"syntology":{"n":6,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":0,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 0 violated, 3 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/roto-translation-equivariant-convolutional#ran","syntology_url":"https://syntology.ai/paper/2002.08725","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2002.08725"}},"official":{"repos":["tueimage/se2cnn"],"state":"official (archive's flag): 3 ran","n_ran":3,"n_constructed":0,"n_ran_no_instrument_failure":3,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/densely-connected-convolutional-networks","slug":"densely-connected-convolutional-networks","title":"Densely Connected Convolutional Networks","date":"2016-08-25","arxiv_id":"1608.06993","repositories_listed":146,"syntology":{"n":71,"n_ran":48,"n_constructed":0,"n_ran_checked":32,"n_instrument":16,"n_unverified":23,"n_honours":1,"n_violates":0,"n_no_contract":31,"n_pointer_only":8,"phrase":"48 ran (of which 0 constructed an object rather than computing a result; 32 with no instrument failure: 1 honoured, 0 violated, 31 with no contract checked; 16 where Syntology's instrument failed) · 23 unverified","sample_list":"/paper/densely-connected-convolutional-networks#ran","syntology_url":"https://syntology.ai/paper/1608.06993","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1608.06993"}},"official":{"repos":["liuzhuang13/DenseNet"],"state":"community repositories only","n_ran":0,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":0,"ran_from_kinds":["listed"]}}}],"record_sha256":"c941cd07fb87c57f175db95ebc97c7a7eeb992eeaadec0a41c92ca2394fb894a","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}