{"id":"W7116735645","doi":"10.1007/978-3-032-13509-4_8","title":"Fuzzy Consensus Clustering for Deep Learning Tuning. Taking Breast Cancer for Medical Diagnosis as a Case","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in social networks","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal; Université du Québec à Rimouski; HEC Montréal","funders":"","keywords":"Deep learning; Artificial neural network; Cluster analysis; Initialization; Fuzzy logic; Medical diagnosis; Supervised learning; Scheme (mathematics)","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":[0.001355136,0.0006598922,0.0009067881,0.0007275267,0.00074499,0.0007821047,0.001601747,0.001586517,0.005299675],"category_scores_gemma":[0.003428959,0.0004263207,0.000735677,0.0007088833,0.000512515,0.000975305,0.001132293,0.001354123,0.001397271],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001324903,"about_ca_system_score_gemma":0.001053139,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0103832,"about_ca_topic_score_gemma":0.01172005,"domain_scores_codex":[0.9996411,0.00007269513,0.00002276389,0.0001160821,0.00008991445,0.00005741528],"domain_scores_gemma":[0.9989634,0.000373223,0.00006414985,0.0001031469,0.0004382889,0.00005789854],"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":[0.0001703077,0.00008719395,0.0007041595,0.0001625165,0.00008231929,0.00005066657,0.0001329844,0.5955374,0.007344641,0.01600789,0.0108503,0.3688696],"study_design_scores_gemma":[0.000003108199,0.00001687419,0.000122457,0.000007261966,0.000005297304,0.0000104992,0.00001494744,0.9930596,0.001347908,0.004759247,0.0006478331,0.000004890938],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01493731,0.0007484368,0.978718,0.0003124159,0.0001134439,0.00004371757,0.0001250788,0.0008930099,0.00410861],"genre_scores_gemma":[0.6017469,0.0004296495,0.3823664,0.0002786414,0.0001216555,0.0001464016,0.0005520238,0.0003858602,0.01397245],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0103832,"threshold_uncertainty_score":0.0206455,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01818222676226625,"score_gpt":0.2970045428563931,"score_spread":0.2788223160941268,"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."}}