{"id":"W4226378137","doi":"10.1016/j.media.2022.102526","title":"Constrained unsupervised anomaly segmentation","year":2022,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"European Regional Development Fund; Generalitat Valenciana; European Commission","keywords":"Computer science; Segmentation; Anomaly detection; Constraint (computer-aided design); Artificial intelligence; Hyperparameter; Regularization (linguistics); Pattern recognition (psychology); Machine learning; 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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0004424718,0.00007906165,0.0001570893,0.0002777212,0.0003628051,0.00007365262,0.0006930695,0.00002804986,0.004649202],"category_scores_gemma":[0.00003549769,0.00007789578,0.0001878335,0.002570451,0.00006961974,0.0001739157,0.0002974956,0.0001680498,0.00002863643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005441,"about_ca_system_score_gemma":0.00007358159,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001281947,"about_ca_topic_score_gemma":0.00001466732,"domain_scores_codex":[0.9985488,0.0001111072,0.0002438696,0.0003131477,0.0006272629,0.0001557777],"domain_scores_gemma":[0.9992716,0.00005323367,0.00007351521,0.0004075638,0.00005189667,0.0001422184],"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":[0.00002610843,0.001733144,0.01387519,0.00003225306,0.002984785,0.0004973289,0.002353515,0.0009972366,0.03657049,0.08613047,0.02684216,0.8279573],"study_design_scores_gemma":[0.0006315411,0.0001669023,0.004286754,0.000001986739,0.0004558975,0.0000530615,0.0004843565,0.9622604,0.007831229,0.00246021,0.0209559,0.0004117748],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01233857,0.00002390511,0.9813775,0.003921125,0.00002814741,0.00009544043,0.000009416964,0.0002884727,0.001917426],"genre_scores_gemma":[0.940564,0.00001097155,0.05696102,0.001657146,0.00002792027,0.0002140398,0.00005460976,0.000005039846,0.0005051944],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9612632,"threshold_uncertainty_score":0.9962607,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006985585779932297,"score_gpt":0.2591613300058138,"score_spread":0.2521757442258815,"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."}}