{"id":"W3010431109","doi":"10.3390/s20051392","title":"Semantically Guided Large Deformation Estimation with Deep Networks","year":2020,"lang":"en","type":"article","venue":"Sensors","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Deutsche Forschungsgemeinschaft","keywords":"Computer science; Artificial intelligence; Segmentation; Deep learning; Parameterized complexity; Inference; Regularization (linguistics); Computer vision; Face (sociological concept); Network architecture; Pattern recognition (psychology); Algorithm","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001802481,0.0000937162,0.0001035554,0.00003699679,0.00007151291,0.0001039618,0.0002712026,0.00004613199,0.00003546073],"category_scores_gemma":[0.000127563,0.00007464627,0.00002337124,0.0003376448,0.00002899392,0.0004635812,0.0000735229,0.0001114689,0.00008307704],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000216539,"about_ca_system_score_gemma":0.00001994387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003194869,"about_ca_topic_score_gemma":0.000002050435,"domain_scores_codex":[0.9990022,0.00006135273,0.0002215078,0.0002026453,0.0003133554,0.0001989061],"domain_scores_gemma":[0.9994262,0.00004161224,0.00008703963,0.0002039894,0.00008816881,0.0001530172],"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":[0.00009034319,0.0002741652,0.001707041,0.0002811834,0.0001279284,0.0002716389,0.01563875,0.2133023,0.002111156,0.05504317,0.02614972,0.6850026],"study_design_scores_gemma":[0.0002366287,0.00007649937,0.0003428067,0.00001392156,0.000005048459,0.00001285957,0.00003498346,0.9942038,0.00464181,0.0002090875,0.0001215071,0.0001010167],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007006639,0.000007615043,0.9886285,0.002821347,0.00004051206,0.0001934463,4.467595e-7,0.0005616689,0.0007398244],"genre_scores_gemma":[0.4210488,0.000005198128,0.575128,0.003729199,0.00004099709,0.000007544509,0.00001309798,0.000007631146,0.00001949757],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.7809016,"threshold_uncertainty_score":0.3043987,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01392545824852605,"score_gpt":0.2571882668844139,"score_spread":0.2432628086358878,"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."}}