{"id":"W2174957696","doi":"10.1504/ijamc.2009.026850","title":"MRI image enhancement by PROPELLER data fusion","year":2009,"lang":"en","type":"article","venue":"International Journal of Advanced Media and Communication","topic":"Advanced MRI Techniques and Applications","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Propeller; Computer vision; Image (mathematics); Image fusion; Artificial intelligence; Image enhancement; Sensor fusion; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0008249183,0.0006501578,0.0006297033,0.001013414,0.0002426607,0.0007347689,0.0007086333,0.000675742,0.001913925],"category_scores_gemma":[0.001915693,0.000320178,0.0004794457,0.0009970024,0.0003635831,0.001153166,0.001023951,0.0006334335,0.00107924],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00019239,"about_ca_system_score_gemma":0.0003325856,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003959173,"about_ca_topic_score_gemma":0.000600709,"domain_scores_codex":[0.999463,0.0001090667,0.00003424133,0.00009066618,0.0002615863,0.00004136103],"domain_scores_gemma":[0.9994424,0.0001765641,0.00007820487,0.0001120248,0.0001709548,0.00001987688],"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":[0.0005049499,0.00006920397,0.0007593556,0.0003147336,0.00007605521,0.0004856797,0.0001624265,0.03508818,0.3173399,0.005668389,0.003179332,0.6363519],"study_design_scores_gemma":[0.00004199666,0.0004427365,0.002119413,0.00005232458,0.00009334441,0.003128073,0.00007728257,0.560575,0.3904677,0.007149914,0.03573868,0.0001136956],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0142041,0.0005717376,0.9824509,0.0002102828,0.0000602051,0.00004163387,0.0000717794,0.000813847,0.001575419],"genre_scores_gemma":[0.1536645,0.001134794,0.8413246,0.0002129992,0.0001238207,0.0000552183,0.0003457069,0.000206512,0.002931832],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001913925,"threshold_uncertainty_score":0.006402731,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02176616019722222,"score_gpt":0.3596353740057153,"score_spread":0.3378692138084931,"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."}}