{"id":"W3169779961","doi":"10.1109/tip.2021.3086053","title":"Robust Segmentation-Free Algorithm for Homogeneity Quantification in Images","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Image Processing","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Homogeneity (statistics); Robustness (evolution); Computer science; Segmentation; Algorithm; Image segmentation; Artificial intelligence; Pattern recognition (psychology); Data mining; Machine learning","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001921771,0.0001479902,0.0001408692,0.0001158534,0.0001699415,0.0001147126,0.0001571591,0.00009634754,0.00001835759],"category_scores_gemma":[0.00002748022,0.0001694927,0.0001212602,0.0002906127,0.00006226928,0.00003280382,0.000003430241,0.0001036784,0.000003830024],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003839101,"about_ca_system_score_gemma":0.0001223333,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001894051,"about_ca_topic_score_gemma":0.0001439429,"domain_scores_codex":[0.9989073,0.00004911219,0.0002653475,0.000466074,0.0001215754,0.0001906007],"domain_scores_gemma":[0.9991479,0.00001838159,0.00008385176,0.0004048608,0.0003050713,0.00003994478],"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":[0.00001528705,0.0001914792,0.00001470797,0.00004222958,0.00002027652,0.000003688786,0.00002742996,0.0005615876,0.764191,2.828149e-7,0.00046211,0.2344699],"study_design_scores_gemma":[0.0004951897,0.00004293762,0.00006884847,0.00003081641,0.00005146784,0.00001172862,0.0001620401,0.01000396,0.9886613,0.00005482031,0.0002335047,0.0001833623],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006939811,0.0004264159,0.9919431,0.0001303243,0.00003609944,0.0002145832,0.00004375293,0.00004258574,0.0002233393],"genre_scores_gemma":[0.4519563,0.0003089076,0.5455709,0.0002029613,0.00008231599,0.0003212723,0.0002136684,0.00005072253,0.001292983],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4463722,"threshold_uncertainty_score":0.6911712,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01971332930716079,"score_gpt":0.292252860726758,"score_spread":0.2725395314195972,"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."}}