{"id":"W4287844213","doi":"10.1016/j.artmed.2022.102368","title":"Evolutionary deep feature selection for compact representation of gigapixel images in digital pathology","year":2022,"lang":"en","type":"article","venue":"Artificial Intelligence in Medicine","topic":"AI in cancer detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo; McMaster University; Brock University; Ontario Tech University","funders":"Ontario Research Foundation","keywords":"Computer science; Digital pathology; Artificial intelligence; Pattern recognition (psychology); Deep learning; Salient; Feature selection; Representation (politics); Feature (linguistics); Visualization; Set (abstract data type); Feature vector; Evolutionary algorithm; Feature extraction; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003195175,0.0003885534,0.0005220188,0.0007887753,0.0001959337,0.0005924315,0.000618914,0.0005526398,0.00178547],"category_scores_gemma":[0.0009624261,0.0001868356,0.0004111218,0.0007464916,0.0002378334,0.0005519465,0.0006322143,0.0005582503,0.0003574953],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004321476,"about_ca_system_score_gemma":0.0005512935,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0030216,"about_ca_topic_score_gemma":0.003578934,"domain_scores_codex":[0.9998907,0.00001758171,0.000005574936,0.00002264044,0.00003241176,0.00003101927],"domain_scores_gemma":[0.9997359,0.00009703956,0.00002852661,0.00003252747,0.0000852169,0.00002078007],"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":[0.0004872745,0.0002037037,0.00312831,0.0001370948,0.00008076135,0.000287015,0.0000998723,0.2516343,0.06133112,0.006227862,0.006344575,0.670038],"study_design_scores_gemma":[0.000007846651,0.00004252159,0.000787346,0.000007105217,0.00001243618,0.0000654163,0.00001730603,0.9915178,0.00559997,0.001323577,0.0006132892,0.000005286508],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2824228,0.0008002944,0.7123118,0.0005434448,0.0001018558,0.00006268755,0.0005245254,0.001271051,0.00196158],"genre_scores_gemma":[0.8525674,0.0003070322,0.1425867,0.0001620439,0.00004361561,0.00006611102,0.0008168948,0.0001150761,0.003335218],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0030216,"threshold_uncertainty_score":0.006008029,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04391706750782386,"score_gpt":0.342908389920368,"score_spread":0.2989913224125441,"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."}}