{"id":"W4308519457","doi":"10.1016/j.jtbi.2022.111342","title":"Deep learning characterization of brain tumours with diffusion weighted imaging","year":2022,"lang":"en","type":"article","venue":"Journal of Theoretical Biology","topic":"Mathematical Biology Tumor Growth","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"St. Michael's Hospital; University of Toronto; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Artificial intelligence; Glioblastoma; Computer science; Preprocessor; Deep learning; Machine learning; Segmentation; Pipeline (software); Medicine; Cancer research","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001400238,0.0001525223,0.0005424632,0.0001937101,0.000123893,0.000007312423,0.0003332457,0.00005999079,0.001436297],"category_scores_gemma":[0.001283256,0.00009888234,0.0001260595,0.0002072926,0.0005952336,0.00004737729,0.0001969825,0.0006214614,0.000002601313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005319217,"about_ca_system_score_gemma":0.00003771509,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":3.874538e-7,"about_ca_topic_score_gemma":1.177393e-7,"domain_scores_codex":[0.99777,0.0008808608,0.0007095553,0.0001525566,0.0002295354,0.0002574667],"domain_scores_gemma":[0.9972274,0.001502603,0.0008637063,0.0001533926,0.000160501,0.00009241315],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0004715757,0.0002273836,0.0082508,0.00003883665,0.00004428825,0.00003669869,0.0003132278,0.000001742414,0.1464213,0.8431846,0.00002413719,0.0009854034],"study_design_scores_gemma":[0.001458501,0.002013673,0.001729119,0.0000724642,0.0001279875,0.001311926,0.0004566622,0.004438312,0.008894815,0.9789243,0.0003495749,0.0002226466],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9429094,0.00003109904,0.05348247,0.002475446,0.0001266031,0.0001214695,0.000005602596,0.00002092021,0.0008269949],"genre_scores_gemma":[0.9948468,0.000006827434,0.004813049,0.0001852806,0.00007342703,0.000005297715,0.00001208628,0.0000224472,0.00003479844],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1375265,"threshold_uncertainty_score":0.9994766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007189347744767782,"score_gpt":0.2556559705335578,"score_spread":0.24846662278879,"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."}}