{"id":"W3092248730","doi":"10.1007/978-3-030-59830-3_56","title":"An Integrated Deep Architecture for Lesion Detection in Breast MRI","year":2020,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University; National Research Council Canada; University of Ottawa","funders":"","keywords":"Computer science; Artificial intelligence; Detector; Object detection; Process (computing); Computer vision; Architecture; Deep learning; Set (abstract data type); Object (grammar); Pattern recognition (psychology); Operating system","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":[0.0003364201,0.0007367915,0.0005459772,0.000451568,0.0002129724,0.0006421703,0.001415445,0.0007992274,0.004948899],"category_scores_gemma":[0.0005473063,0.0004500467,0.0005219628,0.0005065707,0.0001461657,0.0006193379,0.001059836,0.0007463457,0.001970128],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000429144,"about_ca_system_score_gemma":0.0006904615,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004557662,"about_ca_topic_score_gemma":0.01279481,"domain_scores_codex":[0.9998516,0.00001571834,0.00000736534,0.00003579443,0.00005838442,0.00003109352],"domain_scores_gemma":[0.9998339,0.00004572019,0.00001082956,0.00002767825,0.00006525367,0.0000165628],"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":[0.0002508516,0.0001648647,0.0009743682,0.0001811941,0.0001495327,0.0001041574,0.00004362944,0.04523837,0.06716966,0.002646519,0.01245747,0.8706194],"study_design_scores_gemma":[0.00001732436,0.0002160994,0.001261585,0.00004019315,0.0001059404,0.0002085592,0.00002177498,0.9487386,0.03707549,0.003817721,0.008471685,0.00002502158],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03358938,0.002186891,0.9509208,0.0003186299,0.0002160927,0.0000888085,0.0008918759,0.007396774,0.00439076],"genre_scores_gemma":[0.3243122,0.001814605,0.6438898,0.0007700393,0.000166262,0.0001748749,0.002343482,0.0004664967,0.0260621],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004948899,"threshold_uncertainty_score":0.01655567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01454737943840253,"score_gpt":0.2469159469317833,"score_spread":0.2323685674933808,"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."}}