{"id":"W4379031899","doi":"10.1007/978-3-658-41657-7_18","title":"Deep Learning Approaches for Contrast Removal from Contrast-enhanced CT","year":2023,"lang":"en","type":"book-chapter","venue":"Informatik aktuell","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; BC Cancer Agency","funders":"","keywords":"Hounsfield scale; Contrast (vision); Computed tomography; Dosimetry; Nuclear medicine; Contrast enhancement; Selective internal radiation therapy; Medicine; Artificial intelligence; Computer science; Radiology; Magnetic resonance imaging","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0001833983,0.0006455982,0.000849298,0.0001552618,0.0002157105,0.0001265054,0.0004156996,0.0001976404,0.0008736638],"category_scores_gemma":[0.00001475807,0.0006393631,0.0004333586,0.00004039215,0.0001346857,0.0004334848,0.00008071632,0.00077332,0.0002337451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009616614,"about_ca_system_score_gemma":0.00006983167,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000563906,"about_ca_topic_score_gemma":0.000008433805,"domain_scores_codex":[0.9979602,0.00001541972,0.0008169034,0.0003929519,0.0002950616,0.0005194966],"domain_scores_gemma":[0.9981488,0.0003966119,0.000764598,0.0004455944,0.0001148145,0.0001296042],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001756636,0.00003377383,0.00005385433,0.0001411511,0.001179144,0.000009859277,0.001256255,0.002437765,0.0002329024,0.324542,0.002274903,0.6676627],"study_design_scores_gemma":[0.002429947,0.0002170223,0.00001668594,0.0004325544,0.0001902433,0.000006122995,0.000415628,0.02706307,0.004094716,0.1290957,0.8343479,0.001690356],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.00006114188,0.0002335708,0.6062449,0.00001925754,0.0002317921,0.001062375,0.0003684455,0.0004922619,0.3912863],"genre_scores_gemma":[0.06410421,0.0005300544,0.1225386,0.0004925963,0.004372301,0.001192818,0.009235629,0.001132005,0.7964018],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.832073,"threshold_uncertainty_score":0.9996058,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02319343401328017,"score_gpt":0.2455315421095663,"score_spread":0.2223381080962862,"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."}}