{"id":"W4403139160","doi":"10.1007/978-3-031-73647-6_3","title":"Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Translation (biology); Artificial intelligence; Ultrasound; Prostate cancer; Image translation; Image synthesis; Computer vision; Image (mathematics); Cancer; Radiology; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003464491,0.0005335443,0.0003539258,0.000227385,0.0002505348,0.0008525975,0.0006701372,0.0008366526,0.005035884],"category_scores_gemma":[0.001291092,0.0002541857,0.0002281325,0.0005658686,0.0003769737,0.00123042,0.0004680023,0.0006480449,0.001429684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002950381,"about_ca_system_score_gemma":0.0001968452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00112102,"about_ca_topic_score_gemma":0.001964475,"domain_scores_codex":[0.9999257,0.00001795721,0.000002761918,0.0000190419,0.00002131101,0.00001322458],"domain_scores_gemma":[0.9997081,0.0001798872,0.00002559248,0.00003131515,0.00004357623,0.00001145385],"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":[0.0007049128,0.0001721304,0.0009012322,0.0002563873,0.00006230828,0.0003368675,0.0001082261,0.1225764,0.1469988,0.02519502,0.01091748,0.6917703],"study_design_scores_gemma":[0.00002274957,0.0001388849,0.0008011129,0.0000150864,0.00002761035,0.0001992274,0.00005977028,0.9104152,0.06438298,0.01664331,0.007272562,0.00002143601],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09091538,0.004609237,0.8868163,0.001437146,0.000451285,0.00005811208,0.0002938719,0.003167429,0.01225127],"genre_scores_gemma":[0.6970587,0.0029838,0.2815157,0.0003449504,0.0002632541,0.00007207154,0.0006168989,0.0003372799,0.01680729],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005035884,"threshold_uncertainty_score":0.01684672,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005469149775026477,"score_gpt":0.2509205595863681,"score_spread":0.2454514098113416,"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."}}