{"id":"W3131358397","doi":"10.1007/978-3-030-59716-0","title":"Medical Image Computing and Computer Assisted Intervention – MICCAI 2020, 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part III (CAI applications; image registration; instrumentation and surgical phase detection; navigation and visualization; ultrasound imaging; video image analysis)","year":2020,"lang":"en","type":"other","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Hermeneutics and Narrative Identity","field":"Arts and Humanities","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; University of Toronto","funders":"","keywords":"Artificial intelligence; Image registration; Computer science; Spatial normalization; Medical imaging; Medical physics; Visualization; Computer vision; Medicine; Image (mathematics)","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":[0.002353655,0.001079518,0.0007102387,0.002042864,0.0006827563,0.002440802,0.001085095,0.001302539,0.04831913],"category_scores_gemma":[0.002613609,0.0003989342,0.0003725004,0.001660128,0.0008645444,0.001647917,0.002225584,0.001180754,0.02115479],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001444375,"about_ca_system_score_gemma":0.003112182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01028005,"about_ca_topic_score_gemma":0.01262954,"domain_scores_codex":[0.9993293,0.0001251154,0.0000331419,0.0001133553,0.000304706,0.00009442717],"domain_scores_gemma":[0.9983735,0.0002469909,0.00004422011,0.00008942501,0.0008920699,0.000353755],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001976898,0.0001278289,0.0004874147,0.0003509451,0.00002386554,0.00005790122,0.00007107113,0.0009543804,0.004625298,0.009840918,0.6805459,0.3027167],"study_design_scores_gemma":[0.00001758975,0.00007769818,0.003915102,0.000158834,0.00001686437,0.000190487,0.00008925572,0.008233774,0.004320099,0.003707185,0.9792492,0.00002391092],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.02439249,0.1460639,0.1965342,0.03602195,0.04072566,0.0009014231,0.01132793,0.01420128,0.5298312],"genre_scores_gemma":[0.03999921,0.04326181,0.06482679,0.001256645,0.004204984,0.0004354328,0.01232957,0.002010783,0.8316748],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.04831913,"threshold_uncertainty_score":0.1616436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01141416791769297,"score_gpt":0.2710812031862483,"score_spread":0.2596670352685553,"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."}}