{"id":"W4385286804","doi":"10.26481/dis.20230908mb","title":"Combining deep learning and radiomics-based machine learning to optimize predictions on medical images","year":2023,"lang":"en","type":"dissertation","venue":"","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"L'Alliance Boviteq","funders":"","keywords":"Deep learning; Artificial intelligence; Machine learning; Feature (linguistics); Computer science; Radiomics; Workload; Focus (optics); Data science","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":[0.0009608087,0.000937944,0.0007421697,0.0005269292,0.0001834306,0.001115892,0.0008125989,0.001049562,0.001525205],"category_scores_gemma":[0.004187693,0.0004109896,0.0006559435,0.0004973481,0.0004362017,0.00093361,0.0006825953,0.001746384,0.0006645825],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008776556,"about_ca_system_score_gemma":0.0008158622,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006601444,"about_ca_topic_score_gemma":0.006893434,"domain_scores_codex":[0.9997578,0.00007143242,0.00001228655,0.00006923197,0.00005528415,0.00003398923],"domain_scores_gemma":[0.9987055,0.0009129659,0.0000918574,0.00006359585,0.0001821048,0.00004391524],"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":[0.00007647582,0.00007058975,0.001264541,0.0000662522,0.00005251386,0.00004303776,0.00004102316,0.8680472,0.001699324,0.003460359,0.002790472,0.1223883],"study_design_scores_gemma":[0.000001558814,0.00000887385,0.00006886273,0.000005190366,0.000002521475,0.000004322464,0.000001995758,0.9978991,0.0002939033,0.001578938,0.0001328894,0.000001811128],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07318721,0.00198419,0.9173437,0.002091369,0.0001875615,0.00006710884,0.0004182037,0.001548518,0.003172213],"genre_scores_gemma":[0.8097223,0.001881756,0.179291,0.0005724774,0.0003328731,0.0001315346,0.001206805,0.0002390118,0.006622293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006601444,"threshold_uncertainty_score":0.01312608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009140333739497021,"score_gpt":0.3082388569851575,"score_spread":0.2990985232456605,"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."}}