{"id":"W2039861551","doi":"10.1118/1.4894995","title":"Poster — Thur Eve — 09: Evaluation of electrical impedance and computed tomography fusion algorithms using an anthropomorphic phantom","year":2014,"lang":"en","type":"article","venue":"Medical Physics","topic":"Electrical and Bioimpedance Tomography","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Cancer Agency","funders":"","keywords":"Imaging phantom; Image fusion; Pixel; Electrical impedance tomography; Artificial intelligence; Region of interest; Wavelet; Computer vision; Computer science; Tomography; Nuclear medicine; Algorithm; Mathematics; Image (mathematics); Physics; Medicine; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001859111,0.0006078329,0.0004143467,0.0007616843,0.0001822588,0.000696764,0.0003980749,0.0006021535,0.002197464],"category_scores_gemma":[0.004248168,0.000193332,0.0004982012,0.0004081478,0.0003635573,0.0005894922,0.0005343861,0.0002354095,0.0005464848],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000286276,"about_ca_system_score_gemma":0.0002583754,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006523635,"about_ca_topic_score_gemma":0.0005164064,"domain_scores_codex":[0.9994251,0.0001969624,0.00003519578,0.0001246531,0.0001802037,0.00003794648],"domain_scores_gemma":[0.9990143,0.0004542868,0.00008798225,0.0001301629,0.00024922,0.00006400522],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0056856,0.0009355613,0.00714205,0.0004130318,0.0003539083,0.0007465428,0.0003194863,0.1169035,0.5751678,0.002404023,0.002562542,0.2873659],"study_design_scores_gemma":[0.0002301917,0.005505374,0.02605151,0.00004045714,0.0002483486,0.002391877,0.0001830468,0.5565139,0.4002393,0.001282501,0.007188399,0.0001250714],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6744911,0.0004794763,0.318633,0.0002450679,0.0002179349,0.0003754249,0.0004033287,0.001530605,0.003624089],"genre_scores_gemma":[0.819562,0.0002308516,0.1761534,0.00008242428,0.00004295972,0.0001072599,0.0006670881,0.0002431702,0.002910953],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.002197464,"threshold_uncertainty_score":0.009832025,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02247647404361647,"score_gpt":0.2754278464165109,"score_spread":0.2529513723728944,"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."}}