{"id":"W4387033775","doi":"10.1002/jemt.24426","title":"<scp>DeepHistoNet</scp>: A robust deep‐learning model for the classification of hepatocellular, lung, and colon carcinoma","year":2023,"lang":"en","type":"article","venue":"Microscopy Research and Technique","topic":"AI in cancer detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Scheme for Promotion of Academic and Research Collaboration","keywords":"Artificial intelligence; Computer science; Machine learning; Convolutional neural network; Deep learning; Receiver operating characteristic; Population; Artificial neural network; Robustness (evolution); Identification (biology); Pattern recognition (psychology); Data mining; 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":[0.0003337144,0.0007698797,0.0003532995,0.000481056,0.0002043455,0.0004620795,0.001438553,0.0007233939,0.005529994],"category_scores_gemma":[0.001083863,0.0002425952,0.0005858799,0.0005708212,0.0002741871,0.0005602324,0.0006004494,0.001065916,0.001589043],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006455978,"about_ca_system_score_gemma":0.0007478779,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02059613,"about_ca_topic_score_gemma":0.02539779,"domain_scores_codex":[0.9998686,0.00001768559,0.000007397517,0.00003217072,0.0000521526,0.00002194405],"domain_scores_gemma":[0.9997813,0.00005463327,0.00002424487,0.00002932617,0.00008993787,0.00002059553],"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":[0.0004582969,0.0001786031,0.005703186,0.0004067289,0.0002189571,0.0006838532,0.00007498755,0.3125946,0.01690149,0.004814086,0.2333667,0.4245984],"study_design_scores_gemma":[0.00001768742,0.00005378232,0.001048408,0.00001483448,0.00001486683,0.00007759595,0.00000846593,0.9822511,0.006573324,0.001783574,0.008139323,0.00001701296],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1565868,0.005211809,0.7285376,0.006230631,0.00117304,0.0003876789,0.04463471,0.03718733,0.0200503],"genre_scores_gemma":[0.6864336,0.002415823,0.1878882,0.001853978,0.000321772,0.0004184625,0.07715947,0.0010975,0.04241118],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02059613,"threshold_uncertainty_score":0.04095244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07862777500846391,"score_gpt":0.3476303328677812,"score_spread":0.2690025578593173,"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."}}