{"id":"W4295793620","doi":"10.1007/978-3-031-16431-6_9","title":"Feature Robustness and Sex Differences in Medical Imaging: A Case Study in MRI-Based Alzheimer’s Disease Detection","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":33,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Canadian Institutes of Health Research; University of California, Los Angeles; University of California, San Diego; National Institutes of Health; Northern California Institute for Research and Education","keywords":"Computer science; Robustness (evolution); Artificial intelligence; Convolutional neural network; Logistic regression; Pattern recognition (psychology); Spurious relationship; Feature selection; Machine learning; Medical imaging; Regression; Training set; Statistics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001362552,0.0003983448,0.0006461634,0.001108663,0.0001920245,0.0001135546,0.0004125941,0.0001559801,0.00007470484],"category_scores_gemma":[0.0003675786,0.0003345593,0.0000653062,0.0005048341,0.0007276544,0.0001120585,0.000391355,0.002634225,4.28809e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002430436,"about_ca_system_score_gemma":0.0007093447,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004767076,"about_ca_topic_score_gemma":0.0009163892,"domain_scores_codex":[0.9967026,0.0001264155,0.0003769129,0.001142154,0.00120497,0.000446946],"domain_scores_gemma":[0.9984583,0.0005060874,0.000132072,0.0004471017,0.00004478259,0.0004116299],"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":[0.00008830903,0.0002825593,0.1598479,0.0000997532,0.00001946044,0.05972493,0.001185703,0.01688957,0.000007357902,0.00001710984,0.000007022238,0.7618303],"study_design_scores_gemma":[0.001725932,0.0001905948,0.01358648,0.0005286696,0.00006469391,0.002025592,0.00001407757,0.9809759,0.000005221842,0.0003476437,0.0001806897,0.0003544735],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3173126,0.004618712,0.6582628,0.01576291,0.001588195,0.002118106,0.000007208835,0.0001354181,0.0001941184],"genre_scores_gemma":[0.9944918,0.00004279929,0.003613331,0.001510638,0.0002325948,0.00003448522,0.000007383342,0.00003600192,0.00003103507],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9640864,"threshold_uncertainty_score":0.9999107,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01812255653142363,"score_gpt":0.2913809454110273,"score_spread":0.2732583888796037,"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."}}