{"id":"W4403071580","doi":"10.1007/978-3-031-72384-1_1","title":"A Clinical-Oriented Lightweight Network for High-Resolution Medical Image Enhancement","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Image Enhancement Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"","keywords":"Computer science; Resolution (logic); Image (mathematics); High resolution; Computer vision; Artificial intelligence; Computer graphics (images); Remote sensing; Geology","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":[0.004030781,0.0007456933,0.0008816805,0.0006502665,0.0003237527,0.0006346299,0.004229813,0.0006838536,0.0001003252],"category_scores_gemma":[0.0003047443,0.0006518778,0.0003199606,0.0008942022,0.001058623,0.0007221851,0.002668926,0.001343586,0.0001643923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005213761,"about_ca_system_score_gemma":0.0009452124,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001508955,"about_ca_topic_score_gemma":0.00005450275,"domain_scores_codex":[0.9925479,0.00006590374,0.001443466,0.00267657,0.002044888,0.001221331],"domain_scores_gemma":[0.9959015,0.0009552819,0.0004560188,0.001870874,0.0004811678,0.0003351659],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002506945,0.0001174888,0.000009293224,0.0001588341,0.00005883154,0.000209619,0.0001847338,0.0003688994,0.0001767813,0.328582,0.004963426,0.665145],"study_design_scores_gemma":[0.0004689647,0.0007502764,0.00001324492,0.001780361,0.00003302704,0.00002864207,4.638951e-8,0.476102,0.006017915,0.4733916,0.04045558,0.0009584142],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00001218436,0.001082632,0.9822294,0.003111953,0.008564827,0.001335102,0.00001000618,0.0007213687,0.002932573],"genre_scores_gemma":[0.004275644,0.0003006233,0.9871673,0.002769921,0.002988446,0.0001474801,0.00003099398,0.00007197094,0.002247621],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6641867,"threshold_uncertainty_score":0.9995933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01499314472784809,"score_gpt":0.298016404245675,"score_spread":0.2830232595178269,"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."}}