{"id":"W2161377000","doi":"10.1117/12.594856","title":"Physics-based deformable organisms for medical image analysis","year":2005,"lang":"en","type":"article","venue":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer vision; Segmentation; Computer science; Robustness (evolution); Image segmentation; Medical imaging; Image registration; Market segmentation; Image (mathematics); Biology","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":[0.0007037564,0.0002778946,0.0004136623,0.00009644336,0.00007724857,0.00008540144,0.0009211951,0.0002482179,0.00002755653],"category_scores_gemma":[0.000617729,0.0002368459,0.001248115,0.0004163963,0.0002100174,0.00006638112,0.0001634922,0.0001735677,0.000001174634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008156661,"about_ca_system_score_gemma":0.00006367007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000007127494,"about_ca_topic_score_gemma":9.243345e-7,"domain_scores_codex":[0.9980038,1.494142e-8,0.0005981289,0.0004263945,0.0006096725,0.0003619594],"domain_scores_gemma":[0.9976699,0.00005438442,0.0003187535,0.00009591819,0.001727439,0.0001335804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009028461,0.0001722086,0.0002930706,0.0001756213,0.001463366,3.842005e-8,0.00002847765,0.0002666715,0.9655029,0.02432122,0.007127509,0.0005586614],"study_design_scores_gemma":[0.0007606595,0.0002297871,0.0001190273,0.00004084135,0.0006312079,0.00000290246,0.0001253254,0.09159257,0.8968089,0.0002529718,0.009175605,0.0002601848],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9906144,0.00007285571,0.006005021,0.001581623,0.00002792977,0.0004036408,0.00003538193,0.00005450807,0.001204634],"genre_scores_gemma":[0.6054735,0.0001090971,0.3921074,0.0004984916,0.0009218642,0.0002665011,0.0001291669,0.00008549216,0.0004085152],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3861024,"threshold_uncertainty_score":0.9658296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005219798862865209,"score_gpt":0.2376475349308825,"score_spread":0.2324277360680173,"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."}}