{"id":"W6989441491","doi":"","title":"Automatic high content screening using deep learning","year":2018,"lang":"en","type":"dissertation","venue":"Memorial University Research Repository (Memorial University)","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Western Canada Research Grid; Compute Canada","keywords":"Deep learning; High-content screening; Population; Big data; Segmentation; Feature extraction; Feature (linguistics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009911342,0.001436275,0.001211366,0.00299012,0.0004795198,0.001561337,0.001622073,0.001345737,0.003432421],"category_scores_gemma":[0.001919948,0.0006364429,0.001259388,0.001413463,0.0005768487,0.001241862,0.001462067,0.001082291,0.002446103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001690723,"about_ca_system_score_gemma":0.001289196,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00466138,"about_ca_topic_score_gemma":0.007232377,"domain_scores_codex":[0.9991604,0.0001156641,0.00003955373,0.0002401361,0.000319623,0.0001247066],"domain_scores_gemma":[0.9988978,0.000418239,0.000142541,0.0002129299,0.0002537304,0.00007474722],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0007063716,0.0004111888,0.01259785,0.0006281802,0.0001737236,0.0004206432,0.0001076226,0.08942244,0.1844561,0.006230772,0.02435565,0.6804895],"study_design_scores_gemma":[0.00003204359,0.0001048967,0.003131229,0.00003792662,0.00003793929,0.0001448633,0.00004009034,0.8881946,0.09153436,0.009093598,0.007609475,0.00003889437],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08504528,0.00234048,0.8625017,0.0008498982,0.0001548572,0.0003227265,0.00477324,0.0353515,0.008660346],"genre_scores_gemma":[0.476054,0.001338823,0.4998062,0.0009641864,0.00007823842,0.0004162349,0.01142835,0.0008860753,0.00902791],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00466138,"threshold_uncertainty_score":0.01226705,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0297881763514854,"score_gpt":0.2834198746455422,"score_spread":0.2536316982940568,"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."}}