{"id":"W4380576977","doi":"10.1002/gcc.23177","title":"Machine learning in computational histopathology: Challenges and opportunities","year":2023,"lang":"en","type":"review","venue":"Genes Chromosomes and Cancer","topic":"AI in cancer detection","field":"Computer Science","cited_by":66,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Canadian Institute for Advanced Research","keywords":"Workflow; Digital pathology; Histopathology; Artificial intelligence; Computer science; Machine learning; Context (archaeology); Digitization; Medical physics; Pathology; Computer vision; Medicine; Biology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002986362,0.0007901147,0.001362072,0.002136845,0.0004341448,0.002555467,0.001577429,0.002470279,0.002820002],"category_scores_gemma":[0.004640949,0.0005203814,0.0005244736,0.003269736,0.002164174,0.004640311,0.001375432,0.004623841,0.002206728],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001199877,"about_ca_system_score_gemma":0.002122367,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001701621,"about_ca_topic_score_gemma":0.00207544,"domain_scores_codex":[0.9993681,0.0002611452,0.0000487968,0.00008070029,0.0001945761,0.00004663373],"domain_scores_gemma":[0.9947399,0.003991746,0.0001476861,0.0001591058,0.0007884969,0.0001731269],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00004125227,0.00008897253,0.0004285728,0.006498995,0.00008579127,0.0001196194,0.0001671377,0.002572414,0.0003763275,0.07672791,0.04874931,0.8641437],"study_design_scores_gemma":[0.00001739809,0.00009901291,0.0007643136,0.006055483,0.00004582545,0.0004457774,0.0002278608,0.003314805,0.0003846859,0.1230372,0.8655537,0.00005407788],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.0002130985,0.9872857,0.003719378,0.005938907,0.0005275613,0.000007346681,0.00001763302,0.00002772262,0.00226275],"genre_scores_gemma":[0.00232572,0.9914682,0.003014502,0.001161475,0.001283398,0.00001556806,0.00003179899,0.0000124496,0.0006868707],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.002986362,"threshold_uncertainty_score":0.01579356,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1557736330130398,"score_gpt":0.335010630612856,"score_spread":0.1792369975998162,"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."}}