{"id":"W4387211411","doi":"10.1007/978-3-031-43987-2_74","title":"ALL-IN: A Local GLobal Graph-Based DIstillatioN Model for Representation Learning of Gigapixel Histopathology Images With Application In Cancer Risk Assessment","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"BC Cancer Agency; University of British Columbia","funders":"","keywords":"Histopathology; Computer science; Risk stratification; Artificial intelligence; Machine learning; Directed acyclic graph; Stratification (seeds); Artificial neural network; Pattern recognition (psychology); Pathology; Algorithm; Medicine; Internal 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":[0.001140926,0.001546967,0.001573576,0.001030182,0.0007466933,0.00138802,0.004496565,0.002242546,0.006961191],"category_scores_gemma":[0.002286921,0.0007215305,0.001562488,0.001455442,0.0009179653,0.002559707,0.002879513,0.003222536,0.002405788],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001147654,"about_ca_system_score_gemma":0.001359104,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01274995,"about_ca_topic_score_gemma":0.02009695,"domain_scores_codex":[0.9995654,0.00012129,0.00001986276,0.000125435,0.0001074664,0.00006050285],"domain_scores_gemma":[0.9992639,0.0002998622,0.0000445906,0.0001601913,0.0001712738,0.00006010321],"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":[0.0004202249,0.0002712318,0.0006306876,0.0002195625,0.0001765628,0.0001301211,0.00009925586,0.5388498,0.006683368,0.01745723,0.01955785,0.415504],"study_design_scores_gemma":[0.000008105551,0.0000259255,0.00004936268,0.000005815571,0.00001141204,0.00001287748,0.000006743523,0.9916599,0.001228187,0.005991426,0.0009936809,0.000006436562],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01328688,0.0006822713,0.9768563,0.0004748942,0.0001521925,0.00008053052,0.0009540189,0.005534842,0.001978098],"genre_scores_gemma":[0.3120549,0.0008604539,0.6538237,0.0008467584,0.0002387954,0.0004422469,0.006384178,0.001993406,0.02335564],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01274995,"threshold_uncertainty_score":0.02535146,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02680268247098415,"score_gpt":0.3208773817570254,"score_spread":0.2940746992860412,"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."}}