{"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/prognosis/papers/ran/1","list_of":"/task/prognosis","task":"Prognosis","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":[1,22],"of":22,"counts":{"archive_papers_tagged":900,"with_a_code_link":241,"where_syntology_ran_a_sample":22,"not_listed_spam_title":0,"listed":900,"listed_where_code_ran":22,"where_syntology_ran_a_sample_split":{"with_a_run_with_no_instrument_failure":19,"every_run_a_failure_of_syntologys_instrument":3,"listed_with_a_run_with_no_instrument_failure":19,"listed_every_run_a_failure_of_syntologys_instrument":3,"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/prognosis/papers/ran/1","prev":null,"next":null,"papers":[{"url":"/paper/revisiting-end-to-end-learning-with-slide","slug":"revisiting-end-to-end-learning-with-slide","title":"Revisiting End-to-End Learning with Slide-level Supervision in Computational Pathology","date":"2025-06-03","arxiv_id":"2506.02408","repositories_listed":2,"syntology":{"n":9,"n_ran":6,"n_constructed":4,"n_ran_checked":4,"n_instrument":2,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":9,"phrase":"6 ran (of which 4 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 2 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/revisiting-end-to-end-learning-with-slide#ran","syntology_url":"https://syntology.ai/paper/2506.02408","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2506.02408"}},"official":{"repos":["dearcaat/e2e-wsi-abmilx"],"state":"official (archive's flag): 5 ran","n_ran":5,"n_constructed":3,"n_ran_no_instrument_failure":3,"n_unverified":2,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/multimodal-whole-slide-foundation-model-for","slug":"multimodal-whole-slide-foundation-model-for","title":"Multimodal Whole Slide Foundation Model for Pathology","date":"2024-11-29","arxiv_id":"2411.19666","repositories_listed":2,"syntology":{"n":3,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":3,"phrase":"2 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; 2 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/multimodal-whole-slide-foundation-model-for#ran","syntology_url":"https://syntology.ai/paper/2411.19666","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2411.19666"}},"official":{"repos":["mahmoodlab/titan"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/hacsurv-a-hierarchical-copula-based-approach","slug":"hacsurv-a-hierarchical-copula-based-approach","title":"HACSurv: A Hierarchical Copula-Based Approach for Survival Analysis with Dependent Competing Risks","date":"2024-10-19","arxiv_id":"2410.15180","repositories_listed":1,"syntology":{"n":8,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":8,"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) · 1 unverified","sample_list":"/paper/hacsurv-a-hierarchical-copula-based-approach#ran","syntology_url":"https://syntology.ai/paper/2410.15180","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2410.15180"}},"official":{"repos":["raymvp/hacsurv"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/brain-jepa-brain-dynamics-foundation-model","slug":"brain-jepa-brain-dynamics-foundation-model","title":"Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking","date":"2024-09-28","arxiv_id":"2409.19407","repositories_listed":1,"syntology":{"n":10,"n_ran":7,"n_constructed":0,"n_ran_checked":7,"n_instrument":0,"n_unverified":3,"n_honours":0,"n_violates":0,"n_no_contract":7,"n_pointer_only":10,"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) · 3 unverified","sample_list":"/paper/brain-jepa-brain-dynamics-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2409.19407","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.19407"}},"official":{"repos":["Eric-LRL/Brain-JEPA"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":7,"n_unverified":3,"ran_from_kinds":["official"]}}},{"url":"/paper/interpretable-vision-language-survival","slug":"interpretable-vision-language-survival","title":"Interpretable Vision-Language Survival Analysis with Ordinal Inductive Bias for Computational Pathology","date":"2024-09-14","arxiv_id":"2409.09369","repositories_listed":1,"syntology":{"n":16,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":6,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":16,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 6 unverified","sample_list":"/paper/interpretable-vision-language-survival#ran","syntology_url":"https://syntology.ai/paper/2409.09369","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.09369"}},"official":{"repos":["liupei101/vlsa"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":6,"ran_from_kinds":["official"]}}},{"url":"/paper/bls-gan-a-deep-layer-separation-framework-for","slug":"bls-gan-a-deep-layer-separation-framework-for","title":"BLS-GAN: A Deep Layer Separation Framework for Eliminating Bone Overlap in Conventional Radiographs","date":"2024-09-11","arxiv_id":"2409.07304","repositories_listed":3,"syntology":{"n":3,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":3,"n_pointer_only":3,"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) · 0 unverified","sample_list":"/paper/bls-gan-a-deep-layer-separation-framework-for#ran","syntology_url":"https://syntology.ai/paper/2409.07304","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2409.07304"}},"official":{"repos":["pokeblow/bls-gan"],"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"]}}},{"url":"/paper/guidelines-for-augmentation-selection-in","slug":"guidelines-for-augmentation-selection-in","title":"Guidelines for Augmentation Selection in Contrastive Learning for Time Series Classification","date":"2024-07-12","arxiv_id":"2407.09336","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":1,"n_no_contract":2,"n_pointer_only":1,"phrase":"3 ran (of which 0 constructed an object rather than computing a result; 3 with no instrument failure: 0 honoured, 1 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 3 unverified","sample_list":"/paper/guidelines-for-augmentation-selection-in#ran","syntology_url":"https://syntology.ai/paper/2407.09336","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.09336"}},"official":{"repos":["dl4mhealth/ts-contrastive-augmentation-recommendation"],"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/wsi-vqa-interpreting-whole-slide-images-by","slug":"wsi-vqa-interpreting-whole-slide-images-by","title":"WSI-VQA: Interpreting Whole Slide Images by Generative Visual Question Answering","date":"2024-07-08","arxiv_id":"2407.05603","repositories_listed":1,"syntology":{"n":2,"n_ran":1,"n_constructed":0,"n_ran_checked":0,"n_instrument":1,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"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) · 1 unverified","sample_list":"/paper/wsi-vqa-interpreting-whole-slide-images-by#ran","syntology_url":"https://syntology.ai/paper/2407.05603","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2407.05603"}},"official":{"repos":["cpystan/wsi-vqa"],"state":"official (archive's flag): 1 ran","n_ran":1,"n_constructed":0,"n_ran_no_instrument_failure":0,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/drfuse-learning-disentangled-representation","slug":"drfuse-learning-disentangled-representation","title":"DrFuse: Learning Disentangled Representation for Clinical Multi-Modal Fusion with Missing Modality and Modal Inconsistency","date":"2024-03-10","arxiv_id":"2403.06197","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":0,"n_no_contract":2,"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, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/drfuse-learning-disentangled-representation#ran","syntology_url":"https://syntology.ai/paper/2403.06197","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2403.06197"}},"official":{"repos":["dorothy-yao/drfuse"],"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/feature-re-embedding-towards-foundation-model","slug":"feature-re-embedding-towards-foundation-model","title":"Feature Re-Embedding: Towards Foundation Model-Level Performance in Computational Pathology","date":"2024-02-27","arxiv_id":"2402.17228","repositories_listed":2,"syntology":{"n":24,"n_ran":13,"n_constructed":6,"n_ran_checked":11,"n_instrument":2,"n_unverified":11,"n_honours":0,"n_violates":0,"n_no_contract":11,"n_pointer_only":24,"phrase":"13 ran (of which 6 constructed an object rather than computing a result; 11 with no instrument failure: 0 honoured, 0 violated, 11 with no contract checked; 2 where Syntology's instrument failed) · 11 unverified","sample_list":"/paper/feature-re-embedding-towards-foundation-model#ran","syntology_url":"https://syntology.ai/paper/2402.17228","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2402.17228"}},"official":{"repos":["dearcaat/rrt-mil"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":6,"n_ran_no_instrument_failure":6,"n_unverified":3,"ran_from_kinds":["listed","official"]}}},{"url":"/paper/higt-hierarchical-interaction-graph","slug":"higt-hierarchical-interaction-graph","title":"HIGT: Hierarchical Interaction Graph-Transformer for Whole Slide Image Analysis","date":"2023-09-14","arxiv_id":"2309.07400","repositories_listed":1,"syntology":{"n":6,"n_ran":4,"n_constructed":0,"n_ran_checked":4,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":4,"n_pointer_only":6,"phrase":"4 ran (of which 0 constructed an object rather than computing a result; 4 with no instrument failure: 0 honoured, 0 violated, 4 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/higt-hierarchical-interaction-graph#ran","syntology_url":"https://syntology.ai/paper/2309.07400","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2309.07400"}},"official":{"repos":["hku-medai/higt"],"state":"official (archive's flag): 4 ran","n_ran":4,"n_constructed":0,"n_ran_no_instrument_failure":4,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/conslide-asynchronous-hierarchical","slug":"conslide-asynchronous-hierarchical","title":"ConSlide: Asynchronous Hierarchical Interaction Transformer with Breakup-Reorganize Rehearsal for Continual Whole Slide Image Analysis","date":"2023-08-25","arxiv_id":"2308.13324","repositories_listed":1,"syntology":{"n":3,"n_ran":2,"n_constructed":2,"n_ran_checked":2,"n_instrument":0,"n_unverified":1,"n_honours":0,"n_violates":0,"n_no_contract":2,"n_pointer_only":3,"phrase":"2 ran (of which 2 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified; every one of the 2 samples that ran constructed an object rather than computing a result","sample_list":"/paper/conslide-asynchronous-hierarchical#ran","syntology_url":"https://syntology.ai/paper/2308.13324","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2308.13324"}},"official":{"repos":["hku-medai/conslide"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":2,"n_ran_no_instrument_failure":2,"n_unverified":1,"ran_from_kinds":["official"]}}},{"url":"/paper/on-the-use-of-deep-generative-models-for","slug":"on-the-use-of-deep-generative-models-for","title":"On the use of Deep Generative Models for Perfect Prognosis Climate Downscaling","date":"2023-04-27","arxiv_id":"2305.00974","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":0,"n_no_contract":2,"n_pointer_only":0,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 2 with no instrument failure: 0 honoured, 0 violated, 2 with no contract checked; 0 where Syntology's instrument failed) · 1 unverified","sample_list":"/paper/on-the-use-of-deep-generative-models-for#ran","syntology_url":"https://syntology.ai/paper/2305.00974","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2305.00974"}},"official":{"repos":["jgonzalezab/cvae-pp-downscaling"],"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/modeling-dense-multimodal-interactions","slug":"modeling-dense-multimodal-interactions","title":"Modeling Dense Multimodal Interactions Between Biological Pathways and Histology for Survival Prediction","date":"2023-04-13","arxiv_id":"2304.06819","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 ran (of which 0 constructed an object rather than computing a result; 1 with no instrument failure: 0 honoured, 1 violated, 0 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/modeling-dense-multimodal-interactions#ran","syntology_url":"https://syntology.ai/paper/2304.06819","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2304.06819"}},"official":{"repos":["ajv012/survpath","mahmoodlab/survpath"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/advmil-adversarial-multiple-instance-learning","slug":"advmil-adversarial-multiple-instance-learning","title":"AdvMIL: Adversarial Multiple Instance Learning for the Survival Analysis on Whole-Slide Images","date":"2022-12-13","arxiv_id":"2212.06515","repositories_listed":1,"syntology":{"n":15,"n_ran":10,"n_constructed":0,"n_ran_checked":10,"n_instrument":0,"n_unverified":5,"n_honours":0,"n_violates":0,"n_no_contract":10,"n_pointer_only":0,"phrase":"10 ran (of which 0 constructed an object rather than computing a result; 10 with no instrument failure: 0 honoured, 0 violated, 10 with no contract checked; 0 where Syntology's instrument failed) · 5 unverified","sample_list":"/paper/advmil-adversarial-multiple-instance-learning#ran","syntology_url":"https://syntology.ai/paper/2212.06515","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.06515"}},"official":{"repos":["liupei101/advmil"],"state":"official (archive's flag): 10 ran","n_ran":10,"n_constructed":0,"n_ran_no_instrument_failure":10,"n_unverified":5,"ran_from_kinds":["official"]}}},{"url":"/paper/mesh-neural-networks-for-se-3-equivariant","slug":"mesh-neural-networks-for-se-3-equivariant","title":"Mesh Neural Networks for SE(3)-Equivariant Hemodynamics Estimation on the Artery Wall","date":"2022-12-09","arxiv_id":"2212.05023","repositories_listed":2,"syntology":{"n":8,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":2,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 2 unverified","sample_list":"/paper/mesh-neural-networks-for-se-3-equivariant#ran","syntology_url":"https://syntology.ai/paper/2212.05023","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2212.05023"}},"official":{"repos":["sukjulian/coronary-mesh-convolution"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":2,"ran_from_kinds":["official"]}}},{"url":"/paper/quantifying-health-inequalities-induced-by","slug":"quantifying-health-inequalities-induced-by","title":"Quantifying Health Inequalities Induced by Data and AI Models","date":"2022-04-24","arxiv_id":"2205.01066","repositories_listed":1,"syntology":{"n":6,"n_ran":6,"n_constructed":0,"n_ran_checked":6,"n_instrument":0,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":6,"n_pointer_only":0,"phrase":"6 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 0 violated, 6 with no contract checked; 0 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/quantifying-health-inequalities-induced-by#ran","syntology_url":"https://syntology.ai/paper/2205.01066","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2205.01066"}},"official":{"repos":["knowlab/daindex-framework"],"state":"official (archive's flag): 6 ran","n_ran":6,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/context-aware-health-event-prediction-via","slug":"context-aware-health-event-prediction-via","title":"Context-aware Health Event Prediction via Transition Functions on Dynamic Disease Graphs","date":"2021-12-09","arxiv_id":"2112.05195","repositories_listed":1,"syntology":{"n":7,"n_ran":7,"n_constructed":0,"n_ran_checked":6,"n_instrument":1,"n_unverified":0,"n_honours":0,"n_violates":1,"n_no_contract":5,"n_pointer_only":7,"phrase":"7 ran (of which 0 constructed an object rather than computing a result; 6 with no instrument failure: 0 honoured, 1 violated, 5 with no contract checked; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/context-aware-health-event-prediction-via#ran","syntology_url":"https://syntology.ai/paper/2112.05195","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2112.05195"}},"official":{"repos":["luchang-cs/chet"],"state":"official (archive's flag): 7 ran","n_ran":7,"n_constructed":0,"n_ran_no_instrument_failure":6,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/the-rsna-asnr-miccai-brats-2021-benchmark-on","slug":"the-rsna-asnr-miccai-brats-2021-benchmark-on","title":"The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification","date":"2021-07-05","arxiv_id":"2107.02314","repositories_listed":2,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":0,"n_instrument":2,"n_unverified":0,"n_honours":0,"n_violates":0,"n_no_contract":0,"n_pointer_only":0,"phrase":"2 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; 2 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/the-rsna-asnr-miccai-brats-2021-benchmark-on#ran","syntology_url":"https://syntology.ai/paper/2107.02314","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2107.02314"}},"official":null}},{"url":"/paper/semi-supervised-learning-for-identifying-the","slug":"semi-supervised-learning-for-identifying-the","title":"Semi-supervised Learning for Identifying the Likelihood of Agitation in People with Dementia","date":"2021-05-14","arxiv_id":"2105.10398","repositories_listed":1,"syntology":{"n":20,"n_ran":12,"n_constructed":0,"n_ran_checked":12,"n_instrument":0,"n_unverified":8,"n_honours":0,"n_violates":0,"n_no_contract":12,"n_pointer_only":0,"phrase":"12 ran (of which 0 constructed an object rather than computing a result; 12 with no instrument failure: 0 honoured, 0 violated, 12 with no contract checked; 0 where Syntology's instrument failed) · 8 unverified","sample_list":"/paper/semi-supervised-learning-for-identifying-the#ran","syntology_url":"https://syntology.ai/paper/2105.10398","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2105.10398"}},"official":{"repos":["RoonakR/Agitation_detection"],"state":"official (archive's flag): 12 ran","n_ran":12,"n_constructed":0,"n_ran_no_instrument_failure":12,"n_unverified":8,"ran_from_kinds":["official"]}}},{"url":"/paper/pathomic-fusion-an-integrated-framework-for","slug":"pathomic-fusion-an-integrated-framework-for","title":"Pathomic Fusion: An Integrated Framework for Fusing Histopathology and Genomic Features for Cancer Diagnosis and Prognosis","date":"2019-12-18","arxiv_id":"1912.08937","repositories_listed":1,"syntology":{"n":2,"n_ran":2,"n_constructed":0,"n_ran_checked":1,"n_instrument":1,"n_unverified":0,"n_honours":1,"n_violates":0,"n_no_contract":0,"n_pointer_only":2,"phrase":"2 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; 1 where Syntology's instrument failed) · 0 unverified","sample_list":"/paper/pathomic-fusion-an-integrated-framework-for#ran","syntology_url":"https://syntology.ai/paper/1912.08937","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1912.08937"}},"official":{"repos":["mahmoodlab/PathomicFusion"],"state":"official (archive's flag): 2 ran","n_ran":2,"n_constructed":0,"n_ran_no_instrument_failure":1,"n_unverified":0,"ran_from_kinds":["official"]}}},{"url":"/paper/identifying-the-best-machine-learning","slug":"identifying-the-best-machine-learning","title":"Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge","date":"2018-11-05","arxiv_id":"1811.02629","repositories_listed":1,"syntology":{"n":4,"n_ran":3,"n_constructed":0,"n_ran_checked":3,"n_instrument":0,"n_unverified":1,"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) · 1 unverified","sample_list":"/paper/identifying-the-best-machine-learning#ran","syntology_url":"https://syntology.ai/paper/1811.02629","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1811.02629"}},"official":null}}],"record_sha256":"61e94eafeeb031328cb1207b22b45eaf0afcf7251efa1b89c2a7e03881b9019e","record_changed_at":"2026-09-28","record_changed_at_basis":"first_hashed"}