{"id":"W2963910535","doi":"10.1109/access.2019.2930238","title":"Deep Radiomic Analysis Based on Modeling Information Flow in Convolutional Neural Networks","year":2019,"lang":"en","type":"article","venue":"IEEE Access","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure; Université du Québec à Montréal; McGill University","funders":"McGill University","keywords":"Softmax function; Convolutional neural network; Artificial intelligence; Pattern recognition (psychology); Computer science; Binary classification; Feature extraction; Conditional entropy; Feature (linguistics); Entropy (arrow of time); Contextual image classification; Histogram; Wilcoxon signed-rank test; Local binary patterns; Mann–Whitney U test; Mathematics; Principle of maximum entropy; Image (mathematics); Statistics; 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":[0.0003367644,0.0001163911,0.0002869147,0.0005301557,0.00003959129,0.00007457927,0.0001625663,0.00007800084,0.0001564183],"category_scores_gemma":[0.00007999204,0.0001024802,0.0001201668,0.0006526185,0.00002219098,0.0003997817,0.00001812177,0.0004409235,0.00002373956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001097546,"about_ca_system_score_gemma":0.00004589198,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001604682,"about_ca_topic_score_gemma":0.00001092223,"domain_scores_codex":[0.9989401,0.00004377108,0.0003128353,0.0001696941,0.0002999133,0.0002337631],"domain_scores_gemma":[0.9994571,0.00008893157,0.00007593295,0.0002208016,0.00005700933,0.0001002595],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001076088,0.00002633214,0.08139461,0.00002055502,0.0000481256,0.000005562545,0.00003301667,0.9104351,0.000009750853,0.00001570756,0.00007060091,0.007833084],"study_design_scores_gemma":[0.001563208,0.00004037252,0.01898795,0.00004003036,0.000109843,0.000003722481,0.000009174419,0.9790628,0.000004750525,0.00002454851,0.00005012253,0.0001034587],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5469597,0.00002550015,0.4511253,0.0006325119,0.0003417092,0.0001637825,0.000001108096,0.00003156956,0.0007188129],"genre_scores_gemma":[0.9953958,0.000008237293,0.0008128369,0.003453371,0.0001457481,0.00001025156,0.0001467086,0.00001018326,0.00001690903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4503125,"threshold_uncertainty_score":0.4179022,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01103142961424852,"score_gpt":0.2892656213452136,"score_spread":0.2782341917309651,"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."}}