{"id":"W4206461547","doi":"10.1093/noajnl/vdac001","title":"Integrating morphologic and molecular histopathological features through whole slide image registration and deep learning","year":2022,"lang":"en","type":"article","venue":"Neuro-Oncology Advances","topic":"AI in cancer detection","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre; University of Toronto; University Health Network","funders":"Canadian Institutes of Health Research; Princess Margaret Cancer Foundation","keywords":"Workflow; Digital pathology; Scale-invariant feature transform; Computer science; Artificial intelligence; ATRX; Pathology; Pipeline (software); Deep learning; Pattern recognition (psychology); Feature extraction; Biology; Medicine; Database","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":[0.002193569,0.000850817,0.000585278,0.002484124,0.0003235312,0.0012104,0.001251234,0.0008360171,0.001964594],"category_scores_gemma":[0.003317234,0.0004699079,0.001087992,0.001443296,0.0005379018,0.0008928381,0.001359524,0.001059113,0.001604937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000883364,"about_ca_system_score_gemma":0.001531956,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003031035,"about_ca_topic_score_gemma":0.00540956,"domain_scores_codex":[0.9989133,0.0001791167,0.00009136188,0.0003927084,0.0003228389,0.0001007189],"domain_scores_gemma":[0.9984394,0.0004026954,0.0003228486,0.0003673797,0.0003977644,0.00006999585],"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":[0.0003299219,0.00036887,0.01967931,0.0003308848,0.0003375139,0.0003573169,0.0002433329,0.09689563,0.1978822,0.002661513,0.005865644,0.6750479],"study_design_scores_gemma":[0.00002733924,0.0002689554,0.01570928,0.00003835815,0.0001154883,0.0006916376,0.0001150773,0.8619778,0.1060302,0.008053341,0.006899347,0.0000730816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08579043,0.0003778191,0.9052686,0.000337783,0.0000670567,0.000164736,0.0006117687,0.005928952,0.001452836],"genre_scores_gemma":[0.3957327,0.000352024,0.5988652,0.0001783067,0.00006088042,0.0002603009,0.00204522,0.0005036977,0.002001803],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003031035,"threshold_uncertainty_score":0.01160085,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0101506632032242,"score_gpt":0.2726875042782666,"score_spread":0.2625368410750424,"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."}}