{"id":"W2968433965","doi":"10.1007/978-3-030-27202-9_16","title":"WaveM-CNN for Automatic Recognition of Sub-cellular Organelles","year":2019,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Computer science; Pattern recognition (psychology); Convolutional neural network; Artificial intelligence; Convolution (computer science); Discriminative model; Classifier (UML); Wavelet; Feature vector; Deep learning; Feature extraction; Benchmark (surveying); Transfer of learning; Artificial neural network","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":[0.0003854204,0.0002493867,0.0003417366,0.0002590552,0.00004366523,0.00003984749,0.000540425,0.0003039952,0.00001959099],"category_scores_gemma":[0.0001064836,0.0002363378,0.0001675739,0.0001143423,0.0003007354,0.000007087983,0.0002510666,0.0001436824,0.00001054392],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003613169,"about_ca_system_score_gemma":0.0002201078,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005281606,"about_ca_topic_score_gemma":0.00002365492,"domain_scores_codex":[0.9985121,0.00001444458,0.0003412715,0.0006706303,0.0002358197,0.0002256688],"domain_scores_gemma":[0.998695,0.00007784316,0.0002513056,0.0006627794,0.0002737401,0.00003935046],"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":[0.0000108687,0.00002932245,0.00002143572,0.0001616451,0.00003522654,0.000003782125,0.0000419698,0.0003030361,0.6891199,0.00003663501,0.0001529199,0.3100833],"study_design_scores_gemma":[0.0001689978,0.000265672,0.000006330722,0.000171726,0.00003843361,0.000006880458,1.410633e-7,0.01732439,0.9711844,0.009479227,0.001058045,0.0002957765],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01059579,0.0004717983,0.9872277,0.00003871282,0.0001112257,0.0004730378,0.00001019436,0.00002401126,0.00104754],"genre_scores_gemma":[0.7731783,0.0003407508,0.2234073,0.0006451243,0.0005572368,0.00002104793,0.0004903784,0.00009724622,0.001262616],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7638204,"threshold_uncertainty_score":0.9637578,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01078190778250247,"score_gpt":0.2382626879017375,"score_spread":0.2274807801192351,"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."}}