{"id":"W1978756306","doi":"10.1016/j.optlaseng.2010.06.011","title":"Medical image registration using stochastic optimization","year":2010,"lang":"en","type":"article","venue":"Optics and Lasers in Engineering","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convexity; Image registration; Divergence (linguistics); Tsallis entropy; Artificial intelligence; Entropy (arrow of time); Degenerate energy levels; Thresholding; Image (mathematics); Gaussian; Kullback–Leibler divergence; Computer vision; Algorithm; Pattern recognition (psychology); Physics","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":[0.0002474493,0.00005909269,0.00006078944,0.00008437382,0.00002147849,0.00007678398,0.0001400275,0.00006073282,0.00001483953],"category_scores_gemma":[0.0003000114,0.00006162766,0.000008421773,0.0001375463,0.0000264077,0.0002672431,0.00004899472,0.000181545,4.063021e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009652144,"about_ca_system_score_gemma":0.0000284785,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009122391,"about_ca_topic_score_gemma":0.000005737563,"domain_scores_codex":[0.9994414,0.000005912107,0.0001371364,0.0001261963,0.0001851658,0.0001041706],"domain_scores_gemma":[0.9996955,0.00004549032,0.00002697917,0.0001212418,0.00002291773,0.00008782543],"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":[0.000003038589,0.00007299399,0.00009502069,0.0001040386,0.000009950235,0.00009235901,0.0004628975,0.8792499,0.07375596,0.01929192,0.00005060922,0.02681126],"study_design_scores_gemma":[0.0001099381,0.000008598547,0.00004531201,0.00003085312,0.000001286558,0.00001618204,0.000005688384,0.9970035,0.002657594,0.00005256693,0.000002594348,0.00006589342],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008393526,0.000006365293,0.9910612,0.000113214,0.0001708617,0.00005779479,3.166768e-7,0.00007977187,0.0001169538],"genre_scores_gemma":[0.2029966,0.00001050748,0.7969058,0.00004212563,0.00003030774,0.000003561146,0.000001964671,0.000005182957,0.0000039792],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1946031,"threshold_uncertainty_score":0.2513103,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006380674554696346,"score_gpt":0.2469321932776152,"score_spread":0.2405515187229188,"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."}}