{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001862418,0.0001070926,0.0001365844,0.0002842947,0.0005603267,0.0001273843,0.0004562132,0.0001279484,5.636657e-7],"category_scores_gemma":[0.0001918778,0.00009102229,0.00003201164,0.0007880557,0.0003125922,0.0001657411,0.0003428296,0.0003911943,0.000001597078],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001044918,"about_ca_system_score_gemma":0.0001105777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001066053,"about_ca_topic_score_gemma":0.00003543278,"domain_scores_codex":[0.9986085,0.0001145167,0.0001890329,0.0004155913,0.000279177,0.0003931609],"domain_scores_gemma":[0.9985257,0.0006613767,0.00008202606,0.0003883277,0.0002646013,0.00007799479],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003652611,0.00003798736,0.007518413,0.0002970653,0.00001815216,0.000003644705,0.001832361,0.0004658616,0.9680344,0.004570734,0.005511188,0.01167361],"study_design_scores_gemma":[0.0001595264,0.0002763026,0.001913773,0.00002546719,0.000003702128,0.000007178416,0.00009682954,0.862906,0.1295096,0.003266727,0.001794177,0.00004072397],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1768707,0.0009119427,0.8205162,0.0002771327,0.0000335104,0.00112155,0.000005319858,0.0001504332,0.0001131388],"genre_scores_gemma":[0.9687023,0.000580284,0.02878618,0.00001273116,0.00002534581,0.001015152,0.000005685602,0.00001987698,0.0008524911],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8624401,"threshold_uncertainty_score":0.4309637,"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."}}