{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003799791,0.0001758648,0.0001502242,0.0001630624,0.001297177,0.00005431425,0.0005129049,0.00007598098,0.0001433724],"category_scores_gemma":[0.00001660762,0.0001697723,0.00005388147,0.0004626623,0.0001105339,0.0003323597,0.00003249486,0.0005125069,0.00002755008],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008138484,"about_ca_system_score_gemma":0.0001135162,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003013899,"about_ca_topic_score_gemma":0.000002616256,"domain_scores_codex":[0.998439,0.0001708594,0.0004209532,0.0002776025,0.0004203915,0.0002711772],"domain_scores_gemma":[0.9986884,0.000619264,0.0001724653,0.0003086506,0.00006370438,0.0001474966],"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":[0.00003260612,0.00008273892,0.00002752149,0.000006375433,0.00001286391,0.00000158858,0.0001527512,0.9199154,0.00001801512,0.004048074,0.0001217224,0.07558029],"study_design_scores_gemma":[0.0005800336,0.0008929922,0.0001278503,0.000008953644,0.000007914958,0.00002803667,0.00006593645,0.9900758,0.00007504311,0.00663457,0.001317616,0.000185251],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001714998,0.000009979188,0.995167,0.002020289,0.0003273739,0.0002986916,0.0000161042,0.0001891206,0.0002565006],"genre_scores_gemma":[0.6003343,0.00002365426,0.3942077,0.004828897,0.00007101112,0.0001813135,0.0002900165,0.00001107093,0.00005207691],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6009592,"threshold_uncertainty_score":0.9976971,"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."}}