{"id":"W4220896388","doi":"10.18280/ts.390122","title":"Artificial Intelligence Registration of Image Series Based on Multiple Features","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"People's Government of Jilin Province","keywords":"Artificial intelligence; Fuse (electrical); Computer science; Series (stratigraphy); Image registration; Transformation (genetics); Image fusion; Feature (linguistics); Image (mathematics); Pattern recognition (psychology); Computer vision; Convolutional neural network; Feature detection (computer vision); Image processing; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008732479,0.0006102886,0.000615254,0.002056329,0.0002947792,0.001087643,0.0008346671,0.0006158216,0.001159544],"category_scores_gemma":[0.002799452,0.0003596087,0.0009457663,0.002462216,0.0008197512,0.001836623,0.000865371,0.0008110676,0.0005266385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004705325,"about_ca_system_score_gemma":0.000421018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001104729,"about_ca_topic_score_gemma":0.001112071,"domain_scores_codex":[0.9991946,0.0001151668,0.00006340777,0.0002639228,0.0003171523,0.00004582365],"domain_scores_gemma":[0.9992625,0.0001482376,0.0001597357,0.0002259083,0.0001766679,0.00002698455],"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":[0.0002554271,0.00009624776,0.003858359,0.0003959816,0.0002865688,0.0004343757,0.000457445,0.09726479,0.1418956,0.02245136,0.003373366,0.7292305],"study_design_scores_gemma":[0.00001762633,0.0002098046,0.008240001,0.00004552486,0.0001427688,0.0007722942,0.0002184162,0.8783076,0.07831165,0.01964171,0.01401519,0.0000774873],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0296247,0.0004329804,0.9660686,0.0001734405,0.0001127766,0.00005044735,0.00007520677,0.0009936079,0.002468292],"genre_scores_gemma":[0.5525287,0.00117478,0.4409521,0.0001344038,0.0001578597,0.00009303273,0.0003991571,0.0002895844,0.004270394],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002056329,"threshold_uncertainty_score":0.004618227,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02589897235100058,"score_gpt":0.2743735075492609,"score_spread":0.2484745351982603,"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."}}