{"id":"W4415422329","doi":"10.1016/j.cviu.2025.104522","title":"MOSAIC: A multi-view 2.5D organ slice selector with cross-attentional reasoning for anatomically-aware CT localization in medical organ segmentation","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"King Fahd University of Petroleum and Minerals","keywords":"Segmentation; Pipeline (software); Key (lock); Pattern recognition (psychology); Image segmentation; Medical imaging; Orientation (vector space); Scale-space segmentation","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":[0.0008560744,0.001533027,0.001331609,0.001874706,0.0004681359,0.00141078,0.002287238,0.001294546,0.009925747],"category_scores_gemma":[0.00124356,0.001208668,0.00155717,0.001055843,0.0002878768,0.001088783,0.001934774,0.0008954859,0.002109313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005097154,"about_ca_system_score_gemma":0.001328964,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007919428,"about_ca_topic_score_gemma":0.0191745,"domain_scores_codex":[0.999699,0.00003736036,0.00001751703,0.00007641513,0.0001202206,0.00004948773],"domain_scores_gemma":[0.9996613,0.0001351529,0.00003257455,0.00005287186,0.00006912745,0.00004897291],"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":[0.001674495,0.0002181117,0.002499401,0.0005303842,0.0006587469,0.0005735636,0.0002453947,0.03626556,0.1358922,0.004789,0.03414582,0.7825072],"study_design_scores_gemma":[0.0001342442,0.0001954443,0.002394653,0.0000569657,0.0001885201,0.0006588921,0.00009187762,0.9193304,0.05847033,0.00565502,0.01272233,0.0001014199],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0074376,0.0004599966,0.9554,0.00008392722,0.00003682211,0.00009386109,0.0009611847,0.03485011,0.0006765376],"genre_scores_gemma":[0.0806729,0.0003412625,0.91144,0.0002218444,0.00003979895,0.000137683,0.002011749,0.00361809,0.001516673],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009925747,"threshold_uncertainty_score":0.03320497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02414661260178595,"score_gpt":0.343516442457225,"score_spread":0.319369829855439,"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."}}