{"id":"W4393905672","doi":"10.59275/j.melba.2024-267f","title":"Disentangling Hippocampal Shape Variations: A Study of Neurological Disorders Using Mesh Variational Autoencoder with Contrastive Learning","year":2024,"lang":"en","type":"preprint","venue":"The Journal of Machine Learning for Biomedical Imaging","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"MacEwan University; University of Alberta","funders":"Canadian Institutes of Health Research; Women and Children's Health Research Institute; Canada Research Chairs; Children's Health Research Institute","keywords":"Autoencoder; Hippocampal formation; Neuroscience; Psychology; Graph; Artificial intelligence; Pattern recognition (psychology); Medicine; Computer science; Deep learning; Theoretical computer science","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":"codex-gemma-dda1882f352a","candidate_categories":["research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00151131,0.0003570494,0.0007501068,0.0004043828,0.0003304434,0.00005879433,0.0003565126,0.0001060255,0.00003248669],"category_scores_gemma":[0.001082862,0.0002212106,0.0002581526,0.0004014928,0.0003203354,0.00007217776,0.0004798695,0.00358972,5.617638e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009609218,"about_ca_system_score_gemma":0.0003303157,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000394099,"about_ca_topic_score_gemma":0.000001945191,"domain_scores_codex":[0.9971056,0.0003824905,0.0009996537,0.0003864875,0.0008004236,0.0003253265],"domain_scores_gemma":[0.9967642,0.001169298,0.001269761,0.0002371606,0.0003945318,0.0001650698],"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":[0.007967994,0.006581449,0.3193867,0.001893571,0.003813216,0.0004375185,0.01180233,0.5525597,0.003836393,0.002234737,0.0003023361,0.08918405],"study_design_scores_gemma":[0.001969551,0.00176795,0.004438263,0.0006836951,0.001919767,0.000526752,0.000569636,0.9806255,0.000007673991,0.006823448,0.0004638424,0.000203948],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2996404,0.0007371298,0.6908331,0.006973295,0.000256809,0.001340393,0.00002688823,0.000153805,0.00003811158],"genre_scores_gemma":[0.9544595,0.00009595575,0.04471815,0.0001506215,0.0003845046,0.00003667107,0.0000493931,0.00008234695,0.00002285645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6548191,"threshold_uncertainty_score":0.998709,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03634657959718149,"score_gpt":0.3565907821513885,"score_spread":0.320244202554207,"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."}}