{"id":"W2908522648","doi":"10.1142/s0218001419400172","title":"Cell Phenotype Classification Using Deep Residual Network and Its Variants","year":2019,"lang":"en","type":"article","venue":"International Journal of Pattern Recognition and Artificial Intelligence","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Artificial intelligence; Residual; Computer science; Deep learning; Pattern recognition (psychology); Preprocessor; Convolutional neural network; Segmentation; High-content screening; Residual neural network; Artificial neural network; Machine learning; Cell; Algorithm; 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":[0.0002896384,0.00008929132,0.0001060369,0.00008733755,0.00003324978,0.00007855652,0.0001335322,0.00007128315,0.0001566524],"category_scores_gemma":[0.00005147417,0.00008749065,0.00004689501,0.0000488737,0.00003020206,0.00002765232,0.00006046041,0.0001006861,0.0000229809],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000128512,"about_ca_system_score_gemma":0.00002448724,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001080723,"about_ca_topic_score_gemma":0.00001518696,"domain_scores_codex":[0.9991351,0.00005905976,0.0003640867,0.0001702172,0.0001741524,0.00009734258],"domain_scores_gemma":[0.9991112,0.0000237944,0.0002932356,0.00006743943,0.0004528476,0.00005148157],"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":[0.0001636972,0.00009332415,0.003346554,0.00001304957,0.0001003176,0.00001453739,0.0001063571,0.0001847509,0.7792161,0.00004869687,0.0001303393,0.2165823],"study_design_scores_gemma":[0.0002319519,0.0004616546,0.003912318,0.0001820692,0.0001144877,0.0002274274,0.0005964406,0.03351467,0.9522069,0.007265671,0.0008829046,0.0004035587],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9163987,0.0004207186,0.08256564,0.0001165565,0.0001915754,0.00006927282,0.000006345437,0.000003710724,0.0002274819],"genre_scores_gemma":[0.9971649,0.0006586644,0.00126413,0.0002711665,0.0005592201,9.974673e-7,0.00003831718,0.000009804414,0.00003277128],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2161787,"threshold_uncertainty_score":0.3567765,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05569179239280774,"score_gpt":0.3157371915973122,"score_spread":0.2600453992045044,"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."}}