{"id":"W4280598218","doi":"10.18280/ria.360212","title":"Semantics Convolutional Neural Network for Medical Images Analysis","year":2022,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Convolutional neural network; Computer science; Semantics (computer science); Artificial intelligence; Feature (linguistics); Pattern recognition (psychology); Image (mathematics); False positive paradox; Layer (electronics); Data mining; Deep learning; Semantic feature; Artificial neural network; Noise (video)","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001057101,0.0001333704,0.0002283094,0.0001674824,0.000697805,0.00008665552,0.001130282,0.00005144641,0.0006970703],"category_scores_gemma":[0.0001230195,0.0001483604,0.0002583618,0.001934836,0.0000900902,0.000184984,0.0004862235,0.0002753052,0.00003904162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001477817,"about_ca_system_score_gemma":0.0001175817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003318371,"about_ca_topic_score_gemma":0.00002427629,"domain_scores_codex":[0.9979475,0.000121181,0.0004357272,0.0005284511,0.0005469768,0.0004202262],"domain_scores_gemma":[0.9985968,0.000433903,0.0001486448,0.0005755471,0.0001235681,0.0001215557],"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":[0.00001522061,0.00006302533,0.0007775401,0.0000136044,0.00009791686,0.00001142277,0.0002246347,0.9437417,0.00007936659,0.02542646,0.007293029,0.02225605],"study_design_scores_gemma":[0.00003443347,0.0001269244,0.000137733,0.000004627471,0.00005297879,0.00004002898,0.0001085582,0.9730632,0.001450792,0.005484973,0.01932666,0.0001690548],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002669938,0.0004125906,0.990312,0.00449899,0.001388035,0.0002365311,0.0000182761,0.0001518409,0.0003117307],"genre_scores_gemma":[0.9790316,0.00003120694,0.01846942,0.0006522856,0.0003876604,0.0002048685,0.00002337315,0.00001376421,0.001185811],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9763617,"threshold_uncertainty_score":0.7632431,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03053992316767669,"score_gpt":0.2858623798987183,"score_spread":0.2553224567310416,"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."}}