{"id":"W2947272707","doi":"10.1186/s13104-019-4121-7","title":"BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis","year":2019,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"AI in cancer detection","field":"Computer Science","cited_by":116,"is_retracted":false,"has_abstract":true,"ca_institutions":"Calgary Laboratory Services; University of Calgary","funders":"","keywords":"Breast cancer; Histopathology; H&E stain; Cancer; Medicine; Pathology; Histology; Mammography; Artificial intelligence; Computer science; Immunohistochemistry; 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":[0.001160078,0.001971714,0.001249127,0.004241247,0.001257984,0.001487565,0.003067701,0.003223233,0.007728978],"category_scores_gemma":[0.004008987,0.0005689071,0.001221946,0.00345003,0.000540994,0.0006704336,0.001636469,0.001594384,0.008416785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001719181,"about_ca_system_score_gemma":0.002170285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01960365,"about_ca_topic_score_gemma":0.03944856,"domain_scores_codex":[0.9987485,0.0001697534,0.0001747029,0.0003391975,0.0004136386,0.0001542353],"domain_scores_gemma":[0.9977646,0.000581649,0.0002405297,0.0004615675,0.0006869195,0.0002647646],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0013092,0.0007159673,0.01362731,0.003264032,0.0002705409,0.0009391111,0.0001949337,0.004537059,0.01402279,0.001402709,0.8790602,0.08065626],"study_design_scores_gemma":[0.001332478,0.0005113247,0.09456667,0.0007782355,0.0003557372,0.00435845,0.0006894824,0.03025185,0.01677782,0.004001231,0.8461112,0.000265524],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.03334517,0.003167699,0.00628736,0.001128622,0.0003981829,0.0008828053,0.9431411,0.006015816,0.005633248],"genre_scores_gemma":[0.01809889,0.0004319628,0.01133769,0.0002467259,0.0000483285,0.0006017737,0.9673046,0.0001367981,0.001793132],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01960365,"threshold_uncertainty_score":0.03897905,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1961177858867512,"score_gpt":0.4389822763232035,"score_spread":0.2428644904364524,"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."}}