{"id":"W4392682038","doi":"10.1088/1361-6560/ad3321","title":"Multi-scale adversarial learning with difficult region supervision learning models for primary tumor segmentation","year":2024,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Chongqing; National Natural Science Foundation of China","keywords":"Segmentation; Computer science; Artificial intelligence; Deep learning; Similarity (geometry); Jaccard index; Scale (ratio); Machine learning; Pattern recognition (psychology); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001110563,0.00009398607,0.0001470243,0.00004642268,0.00009734121,0.00001290301,0.0001108319,0.00003073425,3.139253e-7],"category_scores_gemma":[0.00001383478,0.00006410303,0.00001408578,0.0002909897,0.00007937686,0.0002241453,0.00006482628,0.0002049497,7.612314e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002673135,"about_ca_system_score_gemma":0.00001761629,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001246234,"about_ca_topic_score_gemma":0.000007731059,"domain_scores_codex":[0.9992754,0.00005019518,0.0001349081,0.0003397023,0.00005088065,0.0001488799],"domain_scores_gemma":[0.9995327,0.0002684841,0.00004176567,0.00008878778,0.00003888049,0.00002938173],"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":[0.0001337144,0.0001137115,0.003789241,0.0002171227,0.00003278994,0.00001260311,0.006761787,0.1787184,0.1713841,0.04786192,0.0001991341,0.5907755],"study_design_scores_gemma":[0.000829157,0.0004273019,0.0002703119,0.0001089184,0.00001041959,0.0000105589,0.0002463951,0.986425,0.0002690744,0.01057281,0.0007240413,0.0001060354],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06097642,0.0002841818,0.9373773,0.0008844889,0.00009469331,0.0002387929,4.486398e-7,0.00008814184,0.00005548305],"genre_scores_gemma":[0.9106691,0.0002424851,0.08829174,0.0001884787,0.0003622921,0.00009183432,0.00007394154,0.0000112062,0.00006886724],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8496928,"threshold_uncertainty_score":0.2614046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1391433213257106,"score_gpt":0.3539512793568157,"score_spread":0.2148079580311051,"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."}}