{"id":"W4318695000","doi":"10.1117/1.jmi.10.1.017501","title":"Single patch super-resolution of histopathology whole slide images: a comparative study","year":2023,"lang":"en","type":"article","venue":"Journal of Medical Imaging","topic":"AI in cancer detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Magnification; Histopathology; Artificial intelligence; Digital pathology; Image resolution; Image quality; Computer science; Medicine; Pixel; Digital imaging; Computer vision; Digital image; Deep learning; Image processing; Pattern recognition (psychology); Medical physics; Image (mathematics); Pathology","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":[0.002258407,0.0001032363,0.0003457284,0.0003306578,0.00007824339,0.00004367817,0.0008006891,0.00004556687,0.00002191675],"category_scores_gemma":[0.0003727641,0.00008817866,0.00009518886,0.0006469747,0.0001488458,0.0005475173,0.000265158,0.0004407189,0.00002110309],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000193656,"about_ca_system_score_gemma":0.0002000194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000722398,"about_ca_topic_score_gemma":0.00001744174,"domain_scores_codex":[0.9973678,0.0003062762,0.0006443337,0.0001917563,0.001252198,0.0002376308],"domain_scores_gemma":[0.9986677,0.0002500383,0.0004066322,0.0002335156,0.0002914736,0.000150603],"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":[0.0001992316,0.002812708,0.06303422,0.0001389403,0.0001822641,0.005666122,0.05692453,0.001416202,0.1374965,0.0004292326,0.1253593,0.6063407],"study_design_scores_gemma":[0.007251776,0.002493767,0.08740272,0.0008823142,0.0001489456,0.00372563,0.01969551,0.8302059,0.01988163,0.004454527,0.02315994,0.000697409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4196495,0.0004534488,0.5670151,0.01058109,0.001869385,0.0001230258,0.000001227012,0.00007234822,0.0002349425],"genre_scores_gemma":[0.9966725,0.00002108898,0.002862191,0.0001528484,0.0002369273,0.000003191896,3.635372e-7,0.00000710394,0.00004376203],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8287897,"threshold_uncertainty_score":0.3595822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04251881914739823,"score_gpt":0.3364908861053369,"score_spread":0.2939720669579386,"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."}}