{"id":"W4416364784","doi":"10.1148/ryai.240502","title":"Random Convolutions for Domain Generalization of Deep Learning–based Medical Image Segmentation Models","year":2025,"lang":"en","type":"article","venue":"Radiology Artificial Intelligence","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Deutsche Forschungsgemeinschaft","keywords":"Segmentation; Convolution (computer science); Generalization; Dice; Pattern recognition (psychology); Image segmentation; Scale-space segmentation; Medical imaging","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":[0.001984373,0.000825726,0.0006633188,0.0005213184,0.0002523219,0.0007617841,0.001121103,0.001001503,0.001784814],"category_scores_gemma":[0.003687709,0.0005034083,0.001212698,0.0004535228,0.0009262344,0.00140065,0.001211376,0.002109183,0.0006500283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275901,"about_ca_system_score_gemma":0.0009935673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00412521,"about_ca_topic_score_gemma":0.003795388,"domain_scores_codex":[0.9995514,0.0001611732,0.00002734738,0.0001191458,0.00009610967,0.00004476797],"domain_scores_gemma":[0.9989769,0.0004743366,0.0001094023,0.0001988917,0.0001713867,0.0000690331],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001508008,0.00004989285,0.0005959067,0.00007797431,0.00007093118,0.00007951047,0.00006698748,0.8397893,0.01045259,0.07687856,0.002573458,0.06921399],"study_design_scores_gemma":[0.000002832865,0.00001129917,0.00004572928,0.000003703986,0.000003820269,0.00001234503,0.000001678516,0.9904118,0.0009579659,0.00818067,0.0003644861,0.000003581965],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01681364,0.0003056097,0.9802477,0.0004947559,0.00005471406,0.00001983991,0.00009263024,0.0006193488,0.001351708],"genre_scores_gemma":[0.7618599,0.0006294803,0.2288198,0.0006193418,0.000136038,0.0001269346,0.0004828801,0.0003943254,0.006931158],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00412521,"threshold_uncertainty_score":0.01049447,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02848134944934683,"score_gpt":0.3259358960427884,"score_spread":0.2974545465934416,"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."}}