{"id":"W2948704847","doi":"10.1109/access.2019.2920933","title":"Cell-Net: Embryonic Cell Counting and Centroid Localization via Residual Incremental Atrous Pyramid and Progressive Upsampling Convolution","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Reproductive Biology and Fertility","field":"Medicine","cited_by":39,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Hemophilia Society; Pacific Centre for Reproductive Medicine; Simon Fraser University","funders":"","keywords":"Artificial intelligence; Computer science; Pyramid (geometry); Pattern recognition (psychology); Upsampling; Residual; Convolution (computer science); Feature extraction; Convolutional neural network; Mathematics; Algorithm; Artificial neural network; Image (mathematics)","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":[0.0003559456,0.0006266091,0.0005288919,0.0005845724,0.0002252005,0.0006210433,0.001653405,0.0006300419,0.002407341],"category_scores_gemma":[0.0005924781,0.0002708588,0.0003835912,0.00037768,0.0002953545,0.0006414197,0.0009518811,0.0006010074,0.001079393],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006620138,"about_ca_system_score_gemma":0.0008293311,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006381411,"about_ca_topic_score_gemma":0.01159458,"domain_scores_codex":[0.9998127,0.00001222911,0.000006730496,0.00005747916,0.00007807084,0.00003285277],"domain_scores_gemma":[0.9998155,0.00003532058,0.00002161042,0.0000417964,0.00006051736,0.00002526044],"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":[0.000367795,0.0001637272,0.003907796,0.0001260391,0.0001044251,0.000313094,0.00009938504,0.08151481,0.1575197,0.006386399,0.008598155,0.7408986],"study_design_scores_gemma":[0.000008709543,0.00005026586,0.001059511,0.000005435313,0.00001724167,0.0001502884,0.000008727271,0.9601158,0.03490379,0.001278099,0.002389077,0.00001301304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02806433,0.0001820666,0.9650131,0.00008649445,0.00005098712,0.000048671,0.0002279167,0.004776696,0.001549775],"genre_scores_gemma":[0.3475656,0.0002952792,0.6413158,0.0002650044,0.00005482482,0.0001263997,0.001321007,0.0002841238,0.00877201],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006381411,"threshold_uncertainty_score":0.01268858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01473143424194887,"score_gpt":0.2804590505424663,"score_spread":0.2657276163005174,"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."}}