{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000734372,0.001154869,0.0006658961,0.0008473429,0.0002032594,0.0006265852,0.001337725,0.0009257302,0.001301904],"category_scores_gemma":[0.001022924,0.0002383078,0.0008571857,0.0004834325,0.0004589348,0.0008689343,0.0006124405,0.001014972,0.0007524295],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008297988,"about_ca_system_score_gemma":0.0007522781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008334064,"about_ca_topic_score_gemma":0.008491082,"domain_scores_codex":[0.9996641,0.00004677236,0.00001908147,0.000118998,0.00009375367,0.00005732402],"domain_scores_gemma":[0.9995994,0.0001011839,0.00006037494,0.00007485652,0.0001302115,0.0000338819],"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":[0.0005331626,0.0004510094,0.006775236,0.0001967084,0.000189815,0.0002980515,0.0001075786,0.3808252,0.08359121,0.005671405,0.008493225,0.5128675],"study_design_scores_gemma":[0.00001115151,0.000087609,0.0007153859,0.000005940069,0.00001890279,0.00004129029,0.00001072289,0.9858013,0.01091243,0.001478633,0.000904883,0.00001183885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1964729,0.001837184,0.7792809,0.0007312107,0.000179711,0.0001990297,0.001512135,0.01357849,0.006208579],"genre_scores_gemma":[0.7534929,0.0007294311,0.2305817,0.0005363605,0.00006214427,0.0002760226,0.005130058,0.0003049103,0.008886429],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008334064,"threshold_uncertainty_score":0.0165711,"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."}}