{"id":"W2013255532","doi":"10.1118/1.3483094","title":"Feasibility of high temporal resolution breast DCE‐MRI using compressed sensing theory","year":2010,"lang":"en","type":"article","venue":"Medical Physics","topic":"MRI in cancer diagnosis","field":"Medicine","cited_by":62,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Cancer Institute; National Institutes of Health; Susan G. Komen for the Cure","keywords":"Compressed sensing; Motion compensation; Computer science; Temporal resolution; Artificial intelligence; Similarity (geometry); Imaging phantom; Image resolution; Computer vision; Pattern recognition (psychology); Nuclear medicine; Image (mathematics); Physics; Optics; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007675938,0.0001537992,0.0004304594,0.00003377879,0.00006884333,0.000007530981,0.0001275864,0.0001981486,0.0003406843],"category_scores_gemma":[0.0002675345,0.0001330488,0.0001177416,0.0002244484,0.0005816445,0.0000736479,0.0000900916,0.0006536067,0.000009017072],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009354116,"about_ca_system_score_gemma":0.0003013026,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006554892,"about_ca_topic_score_gemma":0.00003523217,"domain_scores_codex":[0.9980945,0.0001145408,0.0003586842,0.0003040774,0.0008669591,0.0002612619],"domain_scores_gemma":[0.9984658,0.0003249827,0.0001556472,0.0005674092,0.0002021749,0.0002839968],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.004133936,0.005488521,0.3024487,0.002492615,0.0005903478,0.0003237903,0.001694373,0.0003366867,0.3374992,0.01287144,0.01763952,0.3144809],"study_design_scores_gemma":[0.02083421,0.0006784839,0.2450846,0.003834632,0.001666637,0.001064315,0.000341938,0.1153928,0.4723703,0.1338724,0.003061132,0.001798551],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9429612,0.0000422331,0.05249663,0.00256786,0.0008148575,0.000346423,0.0000532236,0.00008154329,0.0006360874],"genre_scores_gemma":[0.9928478,0.00001064267,0.005516898,0.0004043331,0.001123689,0.000001989262,0.00004973412,0.00002705598,0.00001788616],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3126823,"threshold_uncertainty_score":0.5425574,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04088938551457143,"score_gpt":0.3299553982081564,"score_spread":0.289066012693585,"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."}}