{"id":"W4414394486","doi":"10.1007/978-3-032-05997-0_13","title":"Contrast-Invariant Self-supervised Segmentation for Quantitative Placental MRI","year":2025,"lang":"en","type":"article","venue":"Lecture notes in computer science","topic":"Fetal and Pediatric Neurological Disorders","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Children’s Health Research Institute; Western University","funders":"","keywords":"Segmentation; Pattern recognition (psychology); Representation (politics); Consistency (knowledge bases); Matching (statistics); Domain (mathematical analysis); Image segmentation; Echo (communications protocol)","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":[0.00123541,0.0007874048,0.0009516363,0.001772783,0.0004459983,0.001273259,0.00144938,0.001172547,0.001685703],"category_scores_gemma":[0.002641424,0.0007022912,0.0009417407,0.001233387,0.0005762203,0.0007487872,0.001082856,0.0009367539,0.001265505],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005790193,"about_ca_system_score_gemma":0.001427113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002794135,"about_ca_topic_score_gemma":0.004635551,"domain_scores_codex":[0.999377,0.0001380299,0.00004091445,0.0001760167,0.0001822551,0.00008585099],"domain_scores_gemma":[0.9988636,0.0003888443,0.0001703219,0.0002178379,0.0003017909,0.00005778585],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0005266269,0.0002107978,0.002476221,0.0004856297,0.0002087418,0.0001953601,0.0001922737,0.06968294,0.1927355,0.00437388,0.00650728,0.7224047],"study_design_scores_gemma":[0.00002050038,0.00009760356,0.002784219,0.00003857986,0.00005572847,0.0005520592,0.00006018104,0.9256763,0.06189063,0.004656277,0.004141408,0.00002653538],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02450851,0.0006576985,0.9705127,0.0001478936,0.00004517798,0.00008370195,0.000277358,0.002703894,0.001063127],"genre_scores_gemma":[0.2925111,0.0007372219,0.6991514,0.0001667656,0.000127776,0.0001941444,0.001628315,0.001393299,0.004089936],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002794135,"threshold_uncertainty_score":0.006533563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0148289059334442,"score_gpt":0.2872871934947516,"score_spread":0.2724582875613074,"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."}}