{"id":"W4409369453","doi":"10.1609/aaai.v39i3.32306","title":"Weakly Supervised Gland Segmentation with Class Semantic Consistency and Purified Labels Filtration","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Guangxi Province; National Natural Science Foundation of China","keywords":"Consistency (knowledge bases); Segmentation; Class (philosophy); Artificial intelligence; Filtration (mathematics); Computer science; Pattern recognition (psychology); Natural language processing; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001657821,0.001637516,0.001432542,0.001462531,0.0007082513,0.001408334,0.002381561,0.002090879,0.001448957],"category_scores_gemma":[0.003746321,0.000623134,0.001528138,0.001094978,0.001515522,0.001987593,0.002385678,0.001756143,0.0007491794],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001011554,"about_ca_system_score_gemma":0.001906072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004766411,"about_ca_topic_score_gemma":0.006701438,"domain_scores_codex":[0.9990082,0.0001904757,0.00004775543,0.000397958,0.0002185654,0.0001370272],"domain_scores_gemma":[0.9986365,0.0003629604,0.0002013334,0.0003800326,0.0003179713,0.0001011658],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0008445306,0.0004112601,0.006047275,0.0003358244,0.0003088547,0.0004227514,0.0005837546,0.2752585,0.1210577,0.01561605,0.008014617,0.5710989],"study_design_scores_gemma":[0.00002214097,0.00008865193,0.001263272,0.00001453865,0.00005419855,0.0001021711,0.00004203906,0.9670752,0.01881256,0.01108116,0.001419736,0.00002435082],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05143431,0.0002187983,0.9438523,0.0002640433,0.00003290617,0.000104163,0.0002021804,0.002483206,0.001408074],"genre_scores_gemma":[0.6394787,0.0002311496,0.3526689,0.0004858181,0.0001606864,0.0002456759,0.001485057,0.0006382421,0.00460577],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004766411,"threshold_uncertainty_score":0.009477317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04124428987364722,"score_gpt":0.2967333376570722,"score_spread":0.255489047783425,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}