{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008006755,0.00144869,0.00102124,0.0007567464,0.0003991952,0.0007993779,0.001590973,0.001442455,0.002054133],"category_scores_gemma":[0.002197367,0.0007509252,0.0009221372,0.000842554,0.0008846928,0.001804611,0.001637491,0.001912846,0.0009119817],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001233304,"about_ca_system_score_gemma":0.001103451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005808576,"about_ca_topic_score_gemma":0.01055176,"domain_scores_codex":[0.9996291,0.00008034481,0.00001668312,0.0001301368,0.00008980223,0.00005386004],"domain_scores_gemma":[0.999499,0.0001868738,0.00008620261,0.0001138958,0.00007860808,0.00003543793],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001499376,0.0000581784,0.0005715542,0.00005479842,0.00006547438,0.00006327877,0.00004018394,0.792195,0.01000923,0.009407488,0.002326886,0.1850579],"study_design_scores_gemma":[0.000002791037,0.00001056773,0.00005394742,0.000002962697,0.000003636229,0.00001010429,0.000002744556,0.99422,0.001363601,0.004053126,0.000272994,0.000003511364],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0129626,0.0002006065,0.9840825,0.0001684168,0.00002970681,0.00002346517,0.0000901268,0.001659246,0.0007833582],"genre_scores_gemma":[0.6098101,0.0004668422,0.3795539,0.0004361462,0.0001148023,0.0001698167,0.001150825,0.0006026839,0.00769488],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005808576,"threshold_uncertainty_score":0.01154953,"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."}}