{"id":"W4362670222","doi":"10.54097/hset.v41i.6747","title":"ML Classification Methods Comparison for Breast Cancer Diagnosis in Clinical Application Field","year":2023,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Field (mathematics); Deep learning; Artificial intelligence; Breast cancer; Machine learning; CAD; Cancer detection; Cancer; Pattern recognition (psychology); Medicine; Engineering","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":[0.003750621,0.001183813,0.001078411,0.005159579,0.0004817168,0.002204652,0.00109482,0.001545338,0.004911175],"category_scores_gemma":[0.009191426,0.0002160901,0.001474509,0.001665745,0.0003462848,0.001415549,0.001107921,0.001164269,0.002799394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001014395,"about_ca_system_score_gemma":0.001138375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004598857,"about_ca_topic_score_gemma":0.003126202,"domain_scores_codex":[0.9975675,0.0005690869,0.0003051587,0.0004550182,0.000877956,0.0002252383],"domain_scores_gemma":[0.9967228,0.001360988,0.0001723132,0.0002098867,0.001367259,0.0001668372],"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":[0.001806579,0.000234375,0.02510427,0.0007702404,0.0003680006,0.0001730733,0.0001287381,0.02816675,0.004188757,0.002819016,0.02148933,0.9147508],"study_design_scores_gemma":[0.0002073261,0.0007211599,0.03095522,0.0003063455,0.0005966707,0.0009339754,0.0004986494,0.9146683,0.01572519,0.008554501,0.02670887,0.0001237313],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1893356,0.07013618,0.6832952,0.007352826,0.003817851,0.0006643773,0.004929176,0.009985456,0.03048335],"genre_scores_gemma":[0.8138003,0.01509043,0.1446458,0.001583432,0.001570045,0.0005112122,0.007338846,0.0005369232,0.01492304],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005159579,"threshold_uncertainty_score":0.01983541,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03834364278375412,"score_gpt":0.3933924767144641,"score_spread":0.35504883393071,"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."}}