{"id":"W4412446698","doi":"10.1109/dsp65409.2025.11075027","title":"Learning Beyond Generated Targets: A Modified SFNet for CBCT Image Enhancement","year":2025,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Computer vision; Image enhancement; Artificial intelligence; Image (mathematics)","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":[0.0006472841,0.001066182,0.000587676,0.000624606,0.0001895736,0.0005173975,0.001361569,0.001177298,0.00191041],"category_scores_gemma":[0.001375831,0.0003104021,0.0006874216,0.0003795569,0.0003663508,0.000839098,0.0006778183,0.0008110796,0.0005627322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007538839,"about_ca_system_score_gemma":0.00075743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009466477,"about_ca_topic_score_gemma":0.01164208,"domain_scores_codex":[0.9998168,0.00002430773,0.000009865822,0.00006406856,0.000050959,0.00003389409],"domain_scores_gemma":[0.9997168,0.0001092118,0.00002401298,0.00003522667,0.00009298516,0.00002181749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002970316,0.0001775256,0.00172131,0.0001323218,0.00009948117,0.0002229958,0.00007001879,0.5319744,0.03191848,0.002298843,0.003313139,0.4277744],"study_design_scores_gemma":[0.000005224676,0.00004791294,0.0002641107,0.000006892003,0.00001294199,0.00003697601,0.00000528898,0.993629,0.004555966,0.000869115,0.0005619063,0.000004634831],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1010823,0.001136294,0.8890057,0.0004393435,0.0001518729,0.0001570725,0.000358762,0.003588674,0.00407996],"genre_scores_gemma":[0.6823233,0.0006146662,0.3061442,0.0005425846,0.00009483017,0.000161954,0.001161268,0.0002764835,0.008680721],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009466477,"threshold_uncertainty_score":0.01882273,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01185725199177786,"score_gpt":0.2661715184589592,"score_spread":0.2543142664671814,"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."}}