{"id":"W3107636056","doi":"10.1109/access.2020.3041519","title":"Iris Segmentation Using Interactive Deep Learning","year":2020,"lang":"en","type":"article","venue":"IEEE Access","topic":"Biometric Identification and Security","field":"Computer Science","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Aalborg Universitet; Deutscher Akademischer Austauschdienst; Indian Statistical Institute; University of Alberta; Uppsala Universitet; Institut national de recherche en informatique et en automatique (INRIA); University Grants Commission; Indian National Science Academy; Meiji University; Intel Corporation","keywords":"Computer science; Deep learning; Segmentation; Artificial intelligence; Machine learning; Component (thermodynamics); Biometrics; IRIS (biosensor); Iris recognition","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":[0.0005920324,0.0006551457,0.0006389569,0.0009074839,0.0003403938,0.001036762,0.001453958,0.0008851725,0.005658712],"category_scores_gemma":[0.001337152,0.0004754644,0.000842202,0.0007383321,0.0005544591,0.00125089,0.001997293,0.0009048794,0.001603557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009764684,"about_ca_system_score_gemma":0.000711841,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004747741,"about_ca_topic_score_gemma":0.009564904,"domain_scores_codex":[0.9996025,0.00005298661,0.00001757045,0.0001487321,0.0001188988,0.00005932725],"domain_scores_gemma":[0.9995582,0.0001548982,0.00004443017,0.0001331408,0.00007572222,0.00003365277],"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":[0.0005653506,0.0002038269,0.002435926,0.0001807172,0.0001320773,0.000305543,0.000184051,0.2402623,0.06411437,0.005267992,0.01094022,0.6754077],"study_design_scores_gemma":[0.000009315065,0.00004361245,0.0005767375,0.00001061092,0.00001015573,0.0001058048,0.00001849369,0.9773849,0.01731145,0.002052612,0.002465043,0.000011294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04466642,0.0004303531,0.935141,0.0002660028,0.00006340243,0.00009879882,0.0005412033,0.01352508,0.005267587],"genre_scores_gemma":[0.472542,0.0003304143,0.513433,0.0004681268,0.00006541554,0.0001842392,0.002361672,0.0007766928,0.009838401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005658712,"threshold_uncertainty_score":0.01893032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08561528923686526,"score_gpt":0.3554876378840964,"score_spread":0.2698723486472311,"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."}}