{"id":"W4408281542","doi":"10.1109/iecon55916.2024.10905705","title":"ChebyRegNet: An Unsupervised Deep Learning Technique for Deformable Medical Image Registration","year":2024,"lang":"en","type":"article","venue":"","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Artificial intelligence; Computer science; Image registration; Computer vision; Deep learning; Unsupervised learning; Image (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":[0.001294016,0.001468345,0.001387375,0.001677354,0.000608909,0.001299432,0.003151328,0.002372173,0.0104042],"category_scores_gemma":[0.002701551,0.001167299,0.00155606,0.00169989,0.0006775186,0.001336574,0.003400319,0.0035634,0.004406212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008456772,"about_ca_system_score_gemma":0.001797027,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007329836,"about_ca_topic_score_gemma":0.01944713,"domain_scores_codex":[0.9993722,0.0001132143,0.00002952217,0.0001362872,0.0002727389,0.00007607158],"domain_scores_gemma":[0.9992915,0.0002150794,0.00007431572,0.0002160061,0.0001432705,0.00005975766],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002968076,0.0001966471,0.0006908294,0.0002275124,0.000291164,0.0001413898,0.00007939489,0.06537536,0.02272361,0.01179441,0.03248933,0.8656935],"study_design_scores_gemma":[0.0000269037,0.0000678034,0.0003319776,0.00002786396,0.00002584469,0.0001614413,0.00001307922,0.9656884,0.0152124,0.006720241,0.01169483,0.00002915971],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002663173,0.0002304411,0.9883741,0.0001490069,0.00006990544,0.00008190938,0.0003776077,0.007182295,0.0008714861],"genre_scores_gemma":[0.05871416,0.0005197678,0.9226378,0.0005087835,0.00007802682,0.0003276982,0.002467303,0.002818828,0.01192763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0104042,"threshold_uncertainty_score":0.03480554,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007894581058762748,"score_gpt":0.2551757099133301,"score_spread":0.2472811288545673,"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."}}