{"id":"W4388101099","doi":"10.18280/ts.400522","title":"Multi-Modal Medical Image Matching Based on Multi-Task Learning and Semantic-Enhanced Cross-Modal Retrieval","year":2023,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Matching (statistics); Modal; Modality (human–computer interaction); Image retrieval; Task (project management); Artificial intelligence; Information retrieval; Image (mathematics); Feature (linguistics); Template matching; Feature extraction; Pattern recognition (psychology); Computer vision; Medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001510543,0.0002853775,0.0002785142,0.0002502788,0.00043899,0.0004643724,0.0006637988,0.0001571635,0.0001771762],"category_scores_gemma":[0.0002263435,0.0002576909,0.0001159812,0.0006544041,0.000192872,0.0004707152,0.0002442132,0.0005086377,0.0001470874],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007158559,"about_ca_system_score_gemma":0.0001327325,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001622907,"about_ca_topic_score_gemma":0.00000225327,"domain_scores_codex":[0.9969264,0.0001973598,0.0004914221,0.0007282493,0.001123019,0.000533611],"domain_scores_gemma":[0.9988559,0.0002918761,0.0001627933,0.000285509,0.0001431332,0.0002608286],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003163377,0.0008454904,0.002109369,0.0002449172,0.00006886988,0.0003113908,0.002224789,0.0005612613,0.9280425,0.001906572,0.0002604548,0.063108],"study_design_scores_gemma":[0.001969239,0.0002659954,0.017422,0.0001093621,0.000008772256,0.000007972631,0.00006666603,0.8804902,0.09880783,0.0001448903,0.0003577179,0.0003492862],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1068945,0.00001954303,0.890246,0.001128128,0.0001332758,0.000319986,0.000007616514,0.001095178,0.0001557273],"genre_scores_gemma":[0.9617121,0.00002782872,0.0372474,0.0003862462,0.00009907214,0.00002891445,0.00002528435,0.00002810376,0.0004450733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.879929,"threshold_uncertainty_score":0.9999875,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02106303875143166,"score_gpt":0.3102289237663226,"score_spread":0.2891658850148909,"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."}}