{"id":"W2023537367","doi":"10.1117/12.806089","title":"Ensemble registration: aligning many multi-sensor images simultaneously","year":2009,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Pairwise comparison; Computer science; Artificial intelligence; Image registration; Cluster analysis; Computer vision; Pattern recognition (psychology); Image (mathematics)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002314729,0.001078588,0.001327411,0.001525202,0.0005637868,0.001189341,0.001622314,0.001338581,0.002023983],"category_scores_gemma":[0.00443598,0.0009215271,0.001157957,0.002791215,0.0006657281,0.003136993,0.002937251,0.001579476,0.001196432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003350293,"about_ca_system_score_gemma":0.0008408236,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001309568,"about_ca_topic_score_gemma":0.002821216,"domain_scores_codex":[0.9977332,0.0005448885,0.0001264107,0.000537297,0.0008857715,0.0001723985],"domain_scores_gemma":[0.9980041,0.0004037617,0.0002544039,0.0009274092,0.0003429904,0.00006734953],"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":[0.0005507201,0.0002351139,0.004177026,0.0002919741,0.0004179393,0.0002713391,0.0007310248,0.08045245,0.2309705,0.007263306,0.004918904,0.6697197],"study_design_scores_gemma":[0.00005255383,0.0006283236,0.0073515,0.00006737313,0.0002594531,0.001211636,0.0005127813,0.7284997,0.2225714,0.01614767,0.02249384,0.0002037597],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02182651,0.0002818842,0.9745125,0.0001259147,0.00008245581,0.00007399395,0.00006517569,0.001591922,0.001439621],"genre_scores_gemma":[0.201095,0.0003705202,0.7953413,0.0001374003,0.00009511147,0.0001448606,0.0003282427,0.0003966237,0.002090938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002314729,"threshold_uncertainty_score":0.0122416,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01526240668869334,"score_gpt":0.2609238214730677,"score_spread":0.2456614147843744,"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."}}