{"id":"W2141386220","doi":"10.1117/12.811733","title":"Influence of nuclei segmentation on breast cancer malignancy classification","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Malignancy; Pattern recognition (psychology); Breast cancer; Feature extraction; Support vector machine; Perceptron; Cancer; Artificial neural network; Machine learning; Medicine; Pathology; Internal medicine","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":[0.002252955,0.0005604359,0.0005365883,0.001192739,0.0003614982,0.001189001,0.0002887388,0.0006955376,0.0006315097],"category_scores_gemma":[0.01002875,0.000212851,0.0005148079,0.0005170506,0.0004403558,0.0006535206,0.0004001693,0.000325096,0.0003942032],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004250801,"about_ca_system_score_gemma":0.0003543904,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002772649,"about_ca_topic_score_gemma":0.003096866,"domain_scores_codex":[0.9983029,0.0004443223,0.0001515736,0.0003090594,0.0006565819,0.0001355217],"domain_scores_gemma":[0.994371,0.003732529,0.0004180517,0.000280006,0.001099673,0.00009873253],"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":[0.003209334,0.0002012936,0.08103471,0.0006067613,0.0003757468,0.0006731205,0.0004511442,0.1150616,0.2531734,0.0006389996,0.001637166,0.5429366],"study_design_scores_gemma":[0.00002725147,0.001423757,0.1458163,0.0001599434,0.000432393,0.00155215,0.0005560385,0.5744258,0.2711182,0.0009244697,0.003462757,0.0001009638],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9090855,0.00325373,0.0829911,0.000284182,0.0001773136,0.00009168411,0.0002860169,0.0008799355,0.002950554],"genre_scores_gemma":[0.9598235,0.0006412967,0.03803291,0.00005665264,0.00003008759,0.00001771094,0.0004395001,0.0001001231,0.0008581781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002772649,"threshold_uncertainty_score":0.01191491,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0118670777757162,"score_gpt":0.2438144032932066,"score_spread":0.2319473255174904,"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."}}