{"id":"W2573532050","doi":"10.1109/isai.2016.0075","title":"A Novel Deep Model for Biopsy Image Grading","year":2016,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Grading (engineering); Convolutional neural network; Artificial intelligence; Computer science; Sigmoid function; Pattern recognition (psychology); Feature extraction; Support vector machine; Benchmark (surveying); Deep learning; Image (mathematics); Artificial neural network","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":[0.0004782729,0.0007323609,0.0005354806,0.0007636175,0.0002294411,0.0007837923,0.001602969,0.001051985,0.002545756],"category_scores_gemma":[0.001196499,0.0003839896,0.0007130703,0.0005331282,0.0002897909,0.001293613,0.0006719723,0.001057895,0.001070785],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001001321,"about_ca_system_score_gemma":0.001110675,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007509298,"about_ca_topic_score_gemma":0.01155582,"domain_scores_codex":[0.9997154,0.00003311615,0.00001719548,0.00007948092,0.0001090159,0.00004575458],"domain_scores_gemma":[0.9997107,0.00006021381,0.00003533595,0.00003605378,0.0001342631,0.00002330042],"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":[0.0002637409,0.00020388,0.004153491,0.0001730729,0.0001334927,0.0002333633,0.00006049299,0.3540199,0.03356433,0.0134351,0.01083015,0.582929],"study_design_scores_gemma":[0.000006821571,0.000032778,0.0004354481,0.00000898699,0.00001646656,0.00005988673,0.000003340959,0.9908605,0.003990082,0.002791853,0.001784779,0.000009047884],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01441035,0.0004127868,0.9806418,0.0003975917,0.000119127,0.00007318056,0.0003500856,0.001772447,0.001822566],"genre_scores_gemma":[0.5736255,0.0008741918,0.4093162,0.0007549262,0.0001812645,0.0002597035,0.0017481,0.0002290806,0.01301091],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007509298,"threshold_uncertainty_score":0.01493114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0323159210229287,"score_gpt":0.2668271930389873,"score_spread":0.2345112720160586,"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."}}