{"id":"W4289643891","doi":"10.1109/tcbb.2022.3195705","title":"RLSegNet: An Medical Image Segmentation Network Based on Reinforcement Learning","year":2022,"lang":"en","type":"article","venue":"IEEE/ACM Transactions on Computational Biology and Bioinformatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Reinforcement learning; Segmentation; Pattern recognition (psychology); Feature (linguistics); Image segmentation; Feature extraction; Scale-space segmentation; Process (computing); Computer vision","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":[0.0006387417,0.001046203,0.0008596817,0.000523744,0.0002736854,0.0005556113,0.001494942,0.001156453,0.002755969],"category_scores_gemma":[0.00171275,0.0004767901,0.0006738909,0.0003147358,0.0005164534,0.001087175,0.0008112627,0.001207235,0.0005716964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001009113,"about_ca_system_score_gemma":0.001025476,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007243766,"about_ca_topic_score_gemma":0.008069626,"domain_scores_codex":[0.9997539,0.0000509486,0.00001299049,0.00009519776,0.00005064573,0.00003637495],"domain_scores_gemma":[0.9996578,0.0001509471,0.00004031508,0.00003485999,0.00008281716,0.0000333023],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003647219,0.0002095319,0.002337966,0.0001578831,0.0001679543,0.0003153946,0.00009624766,0.6418262,0.01177998,0.005749321,0.008350774,0.328644],"study_design_scores_gemma":[0.00001397491,0.00005175233,0.0001527121,0.000006201908,0.00001299511,0.00003236007,0.000003994168,0.9950157,0.001933637,0.002000332,0.0007693915,0.000006968326],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03414957,0.001357617,0.9551029,0.0005464467,0.0001912125,0.0001292367,0.0002321685,0.004819082,0.003471765],"genre_scores_gemma":[0.741598,0.0007718509,0.2475795,0.0007956786,0.0001013526,0.0003102264,0.0009283076,0.0002910205,0.007624142],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007243766,"threshold_uncertainty_score":0.01440316,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0156493142804702,"score_gpt":0.2855532805708636,"score_spread":0.2699039662903934,"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."}}