{"id":"W4386449347","doi":"10.20944/preprints202309.0223.v1","title":"Deep Learning in Medical Image Registration: Introduction and Survey","year":2023,"lang":"en","type":"preprint","venue":"Preprints.org","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Service Public de Wallonie","keywords":"Image registration; Artificial intelligence; Computer science; Computer vision; Affine transformation; Medical imaging; Pyramid (geometry); Image processing; Segmentation; Rotation (mathematics); Image (mathematics); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001642601,0.000877635,0.001281512,0.002377011,0.0002832216,0.00172828,0.001135157,0.00138517,0.002619373],"category_scores_gemma":[0.003162451,0.0007104023,0.000759794,0.004910974,0.0007806933,0.002857012,0.001374516,0.002017398,0.001651513],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001048622,"about_ca_system_score_gemma":0.0008266367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002803683,"about_ca_topic_score_gemma":0.001984954,"domain_scores_codex":[0.9991401,0.000187818,0.0001014274,0.0002062476,0.0003180888,0.00004615746],"domain_scores_gemma":[0.9986264,0.0008468843,0.00007768984,0.0001092705,0.0002905584,0.00004916186],"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":[0.00007730171,0.0001248695,0.001683229,0.002846387,0.0001295316,0.00008171854,0.0001263836,0.01617484,0.001416759,0.0247102,0.01628202,0.9363469],"study_design_scores_gemma":[0.00004756746,0.0006312609,0.007074458,0.00363199,0.0002719635,0.001445089,0.0003654866,0.1962327,0.009307975,0.1207532,0.660033,0.0002053583],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.007499345,0.7157679,0.2548648,0.003854231,0.0007455236,0.0001232757,0.0005813102,0.0005940964,0.01596956],"genre_scores_gemma":[0.06939998,0.8067729,0.1083394,0.001633054,0.002386062,0.0002352636,0.00144917,0.0002482108,0.009535934],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002803683,"threshold_uncertainty_score":0.008762717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1219535894620288,"score_gpt":0.3856695918370166,"score_spread":0.2637160023749878,"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."}}