{"id":"W4412441453","doi":"10.1016/j.bspc.2025.108251","title":"Robust semantic learning for precise medical image segmentation","year":2025,"lang":"en","type":"article","venue":"Biomedical Signal Processing and Control","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Computer vision; Image segmentation; Image (mathematics); Pattern recognition (psychology); Machine learning","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":[0.001442137,0.000963928,0.001908325,0.001716939,0.0005054236,0.001298903,0.001363554,0.001803164,0.001821943],"category_scores_gemma":[0.003451437,0.0008062392,0.001606361,0.001514845,0.001482653,0.001916056,0.002137823,0.001925111,0.0009076085],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009298734,"about_ca_system_score_gemma":0.001459655,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0028813,"about_ca_topic_score_gemma":0.003312302,"domain_scores_codex":[0.9990415,0.0002061879,0.00006540336,0.000285745,0.0002950217,0.0001062025],"domain_scores_gemma":[0.9989675,0.0004366346,0.0001244866,0.0002442319,0.0001876942,0.00003946667],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004581216,0.0001483347,0.0006105981,0.0003496868,0.0001647607,0.0001513698,0.0001350163,0.3121038,0.053565,0.04217929,0.005023434,0.5851106],"study_design_scores_gemma":[0.0000110035,0.00005328994,0.0002193456,0.00001496371,0.0000212496,0.00007659385,0.00001503991,0.9653679,0.008836046,0.02391462,0.001457203,0.00001263023],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003744215,0.0001993198,0.9951521,0.0000825375,0.00001846685,0.00001610316,0.00005075008,0.0004641484,0.0002723305],"genre_scores_gemma":[0.2805852,0.0007381628,0.7139097,0.0003019436,0.000165839,0.0001516178,0.0008795889,0.0005446032,0.002723296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0028813,"threshold_uncertainty_score":0.007626832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242752904050087,"score_gpt":0.285572493129623,"score_spread":0.2731449640891221,"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."}}