{"id":"W4394892271","doi":"10.1007/s00521-024-09729-4","title":"End-to-end dynamic residual focal transformer network for multimodal medical image fusion","year":2024,"lang":"en","type":"article","venue":"Neural Computing and Applications","topic":"Advanced Image Fusion Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation Project of Chongqing, Chongqing Science and Technology Commission; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Residual; Encoder; Convolutional neural network; Image fusion; Deep learning; Transformer; Feature extraction; Pattern recognition (psychology); Image (mathematics); Algorithm; Engineering","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":[0.0003225364,0.0005590119,0.0004664969,0.0003227053,0.0002500227,0.0004348002,0.0008415779,0.0006491965,0.004788577],"category_scores_gemma":[0.0004740391,0.0002141187,0.0003232404,0.0002909437,0.0002056965,0.0007035938,0.0008863111,0.0006702662,0.001279158],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002932674,"about_ca_system_score_gemma":0.0005861296,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002536566,"about_ca_topic_score_gemma":0.006008571,"domain_scores_codex":[0.9998354,0.00002492886,0.000007812099,0.00003455654,0.00006371822,0.00003352738],"domain_scores_gemma":[0.9999022,0.00002039813,0.00000817724,0.00001653891,0.00004312945,0.000009372321],"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":[0.0006751481,0.0001807411,0.0007658577,0.0001126009,0.00009444341,0.0002030932,0.00005677886,0.05758145,0.1015954,0.003477485,0.006605935,0.8286511],"study_design_scores_gemma":[0.00002123494,0.000144058,0.0005296722,0.00001118559,0.00003471284,0.0002726741,0.00002366991,0.947007,0.0466953,0.002862839,0.002381101,0.00001643797],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01530594,0.0002290509,0.981039,0.0001065639,0.00005275839,0.00005604096,0.0001215832,0.001217338,0.001871687],"genre_scores_gemma":[0.5878091,0.0004692379,0.3980358,0.0003054861,0.00006908094,0.0001303167,0.0006546546,0.000155191,0.01237105],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004788577,"threshold_uncertainty_score":0.0160194,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006547348836103625,"score_gpt":0.2889312222107686,"score_spread":0.282383873374665,"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."}}