{"id":"W3091945056","doi":"10.29007/6mkk","title":"A Deep Learning Approach for Single Shot C-Arm Pose Estimation","year":2020,"lang":"en","type":"article","venue":"EPiC series in health sciences","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Artificial intelligence; Deep learning; Computer science; Position (finance); Process (computing); Transfer of learning; Radiography; One shot; Single shot; Computer vision; Range (aeronautics); Pose; Hand position; Shot (pellet); Medicine; Radiology; Engineering; Optics","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":[0.0005741004,0.00006606852,0.0001445278,0.00005745412,0.0001499569,0.00003860646,0.0001165999,0.00002135508,0.00003926783],"category_scores_gemma":[0.0003534313,0.00005893799,0.00002373014,0.0005100726,0.0001044504,0.00019858,0.00001311252,0.0001063574,0.000002917628],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003294323,"about_ca_system_score_gemma":0.00002504386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002922649,"about_ca_topic_score_gemma":0.000002901014,"domain_scores_codex":[0.9991673,0.00003284059,0.0002204542,0.0001465272,0.0001537145,0.0002791435],"domain_scores_gemma":[0.9997017,0.0000782547,0.00003307997,0.00004209804,0.000009072002,0.000135845],"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":[0.000002845255,0.00002557835,0.004043862,0.0005886345,0.000004723826,5.153739e-7,0.004629329,0.9385765,0.0001621641,0.0005160786,0.0001613938,0.05128835],"study_design_scores_gemma":[0.00006817655,0.00008772901,0.000141367,0.00002059108,0.00000224162,0.000001881565,0.001103865,0.9968898,0.0001388481,0.0001358132,0.001343826,0.00006583716],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06493713,0.006342752,0.8990667,0.0136589,0.0007436673,0.0005408079,0.000002456888,0.0007146449,0.01399294],"genre_scores_gemma":[0.9360917,0.000053213,0.06324121,0.0004543785,0.0001086411,0.00002073978,0.000007388061,0.00000586894,0.00001681265],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8711546,"threshold_uncertainty_score":0.2403421,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06026990887733408,"score_gpt":0.3100419094811901,"score_spread":0.2497720006038561,"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."}}