{"id":"W2979634944","doi":"10.1007/978-3-030-32692-0_3","title":"Globally-Aware Multiple Instance Classifier for Breast Cancer Screening","year":2019,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Canadian Institute for Advanced Research","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute","keywords":"Computer science; Artificial intelligence; Classifier (UML); Residual neural network; Mammography; Pixel; Pattern recognition (psychology); Artificial neural network; Breast cancer; Contextual image classification; Computer vision; Machine learning; Image (mathematics); Cancer; 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":[0.0009678141,0.001440907,0.002556284,0.001368578,0.0006477664,0.001220527,0.002382468,0.001786582,0.001882306],"category_scores_gemma":[0.001845171,0.0004516545,0.001239042,0.001019339,0.0001789599,0.001410754,0.001328352,0.00158372,0.001417162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004884457,"about_ca_system_score_gemma":0.001182124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00524666,"about_ca_topic_score_gemma":0.008841624,"domain_scores_codex":[0.9988838,0.0001747952,0.00006469161,0.0004485393,0.000232963,0.0001953724],"domain_scores_gemma":[0.9993099,0.0001947378,0.00005724068,0.0001501221,0.0002154443,0.00007239365],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0010474,0.0007434014,0.01697624,0.0001904272,0.0004613411,0.0004068891,0.00005981205,0.04726715,0.03313583,0.001049645,0.02137858,0.8772833],"study_design_scores_gemma":[0.00003642176,0.0002161523,0.005484639,0.00002369385,0.0002609984,0.0004478294,0.00006690413,0.9728695,0.01495079,0.002348982,0.003265393,0.00002871726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.22576,0.006993777,0.742655,0.001258137,0.0010054,0.0002189349,0.00385535,0.01277508,0.005478279],"genre_scores_gemma":[0.7701343,0.0008133133,0.2150629,0.0005089065,0.0004527461,0.0001180437,0.005978432,0.0004664296,0.00646501],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00524666,"threshold_uncertainty_score":0.01043224,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01585183058171993,"score_gpt":0.2640917233260536,"score_spread":0.2482398927443337,"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."}}