{"id":"W3157093734","doi":"10.1038/s41598-021-88494-z","title":"Overcoming the limitations of patch-based learning to detect cancer in whole slide images","year":2021,"lang":"en","type":"article","venue":"Scientific Reports","topic":"AI in cancer detection","field":"Computer Science","cited_by":55,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sunnybrook Health Science Centre; University of British Columbia; Sunnybrook Hospital; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada","keywords":"Computer science; False positive paradox; Artificial intelligence; Workflow; Context (archaeology); Task (project management); False positives and false negatives; Binary classification; True positive rate; Deep learning; Machine learning; Pattern recognition (psychology); Image (mathematics); Sensitivity (control systems); Support vector machine","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":[],"consensus_categories":[],"category_scores_codex":[0.001288729,0.00008527609,0.0001239574,0.0001722006,0.000277083,0.000303544,0.0002761902,0.00003052663,0.00001511684],"category_scores_gemma":[0.0006833008,0.00007330605,0.00006569298,0.00180666,0.00008240604,0.0002851484,0.0001701909,0.0001594491,0.000006785764],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001386411,"about_ca_system_score_gemma":0.0005195108,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002712799,"about_ca_topic_score_gemma":0.001543371,"domain_scores_codex":[0.9982355,0.0001169949,0.0003750937,0.0005854691,0.0004579939,0.0002288983],"domain_scores_gemma":[0.9984209,0.0001964979,0.0002224762,0.0008324714,0.0002741392,0.00005355019],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00000495941,0.00003729239,0.01479965,0.00002624742,0.00001248878,0.0002960271,0.0032801,0.2060511,0.4876259,0.00005956712,0.004274111,0.2835325],"study_design_scores_gemma":[0.0001272583,0.00003422037,0.01659274,0.0001463919,0.000009006078,0.00004958221,0.0002820553,0.02232856,0.8672065,0.003830911,0.08919426,0.0001985263],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6527306,0.0006572519,0.3303975,0.005509629,0.009295699,0.0003939175,0.000002676638,0.000136107,0.0008765738],"genre_scores_gemma":[0.9847414,0.000005484034,0.01386402,0.00008607265,0.00002892143,0.00005961987,0.000001542418,0.000007651962,0.001205319],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3795806,"threshold_uncertainty_score":0.2989334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03099410911131165,"score_gpt":0.2755036914624582,"score_spread":0.2445095823511466,"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."}}