{"id":"W2898859555","doi":"10.1109/embc.2018.8512285","title":"Fully Convolutional DenseNets for Segmentation of Microvessels in Two-photon Microscopy","year":2018,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"Canadian Institutes of Health Research","keywords":"Segmentation; Convolutional neural network; Microscopy; Two-photon excitation microscopy; Artificial intelligence; Computer science; Optical microscope; Computer vision; Multiphoton fluorescence microscope; Image segmentation; Deep learning; Pattern recognition (psychology); Photon; Optics; Materials science; Biomedical engineering; Physics; Medicine; Fluorescence microscope; Scanning electron microscope","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0001695016,0.00005911288,0.0001564399,0.0001045953,0.0000213804,0.000005883078,0.00002826009,0.00001815405,0.0001342104],"category_scores_gemma":[0.00004120581,0.00005019786,0.00006113638,0.0001277608,0.00008914319,0.00003199139,0.000007144565,0.0000296441,0.000013602],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003372942,"about_ca_system_score_gemma":0.00005571611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002978675,"about_ca_topic_score_gemma":0.00006934001,"domain_scores_codex":[0.9994538,0.00001507947,0.0002011528,0.0001304165,0.00008773465,0.0001118515],"domain_scores_gemma":[0.9995902,0.00004857117,0.00005821637,0.00008443028,0.0001859665,0.00003260316],"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":[0.0001625332,0.00006537733,0.05445501,0.00004319079,0.00002862041,0.000001054954,0.00009331323,0.00000162719,0.9426865,0.0001678401,0.001676965,0.0006179495],"study_design_scores_gemma":[0.001754346,0.0001683854,0.01509475,0.00007255132,0.00006978029,0.00001414005,0.0001864362,0.002896101,0.9786341,0.0002990648,0.0007509599,0.00005942431],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982904,0.00007704701,0.01482926,0.0005008637,0.00004579859,0.000190285,0.000007400514,0.00001106027,0.001434244],"genre_scores_gemma":[0.9553146,0.000006481827,0.04227573,0.0003965514,0.00007240559,0.000009741831,0.00004208962,0.000006497523,0.001875888],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03936026,"threshold_uncertainty_score":0.2047009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01627670313280448,"score_gpt":0.3735439469700053,"score_spread":0.3572672438372008,"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."}}