{"id":"W2024384381","doi":"10.1088/0031-9155/52/13/010","title":"Evaluating an optical-flow-based registration algorithm for contrast-enhanced magnetic resonance imaging of the breast","year":2007,"lang":"en","type":"article","venue":"Physics in Medicine and Biology","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":45,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre","funders":"Canadian Institutes of Health Research; Johnson and Johnson","keywords":"Contrast (vision); Computer science; Optical flow; Magnetic resonance imaging; Computer vision; Voxel; Intensity (physics); Dynamic contrast; SIGNAL (programming language); Image registration; Displacement (psychology); Artificial intelligence; Algorithm; Breast MRI; Breast cancer; Mammography; Optics; Image (mathematics); Physics; Radiology; Medicine; Cancer","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004124131,0.0007949023,0.00078044,0.0009248128,0.0004326028,0.001208183,0.001175681,0.001561765,0.0009324156],"category_scores_gemma":[0.01085717,0.0003560883,0.0005986879,0.0007666404,0.0004020956,0.001240549,0.000532032,0.0005286547,0.0003525112],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007319291,"about_ca_system_score_gemma":0.0016395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00476471,"about_ca_topic_score_gemma":0.003837788,"domain_scores_codex":[0.9985708,0.0005738061,0.000116927,0.0001844455,0.0004680597,0.00008596862],"domain_scores_gemma":[0.9972808,0.001436946,0.0001994952,0.0001966103,0.0008273904,0.00005861207],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001633332,0.0008153958,0.004270462,0.0002482324,0.0002358739,0.0001201802,0.0001585764,0.4786524,0.07503339,0.003318454,0.001002308,0.4345115],"study_design_scores_gemma":[0.00006837673,0.0005815867,0.001142099,0.000006357534,0.00003729748,0.0000766521,0.0000269381,0.9693404,0.0277724,0.0003683738,0.0005571325,0.00002241447],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2955028,0.0004940975,0.6997409,0.00022433,0.00009679334,0.0006512608,0.00007099282,0.00179347,0.001425394],"genre_scores_gemma":[0.372122,0.0002503461,0.6255444,0.0000651572,0.00002269957,0.0002236572,0.0002427256,0.0002102939,0.001318672],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00476471,"threshold_uncertainty_score":0.02181077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1486982621233438,"score_gpt":0.439720842421399,"score_spread":0.2910225802980552,"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."}}