{"id":"W3154477696","doi":"10.48550/arxiv.2107.12978","title":"Optimizing Operating Points for High Performance Lesion Detection and Segmentation Using Lesion Size Reweighting","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Lesion; Scanner; Entropy (arrow of time); Computer vision; Medicine; Pathology","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":[0.004644546,0.001964614,0.00148278,0.001869238,0.000594972,0.001401891,0.001483114,0.002098871,0.00187388],"category_scores_gemma":[0.01111793,0.0005922666,0.0008022637,0.0009418933,0.001078019,0.001625337,0.001342102,0.001918574,0.001347437],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000784756,"about_ca_system_score_gemma":0.0009819149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002530701,"about_ca_topic_score_gemma":0.002450144,"domain_scores_codex":[0.9986755,0.0003627102,0.0000864227,0.0003910117,0.0003450897,0.0001392139],"domain_scores_gemma":[0.9962667,0.002060452,0.0004382001,0.0003316277,0.000734913,0.0001680646],"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":[0.0008986795,0.0003769254,0.004419193,0.0001933828,0.0001819995,0.0003102571,0.0002814371,0.3901283,0.05869806,0.005353308,0.005153581,0.5340049],"study_design_scores_gemma":[0.00003090311,0.0001194806,0.0008744983,0.0000138914,0.00001927791,0.0000963582,0.00003001735,0.9842068,0.008695788,0.005227551,0.0006676075,0.00001793242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.06455442,0.0007527933,0.930788,0.0003214322,0.00005009706,0.00009611451,0.00007992931,0.002557303,0.0007998936],"genre_scores_gemma":[0.4748269,0.00040684,0.5196782,0.0003111413,0.0001441439,0.0002283844,0.0004697276,0.0007537876,0.003180772],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004644546,"threshold_uncertainty_score":0.02456295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08694388454999236,"score_gpt":0.2193217479165081,"score_spread":0.1323778633665158,"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."}}