{"id":"W4380272571","doi":"10.1101/2023.06.09.544263","title":"Gauge equivariant convolutional neural networks for diffusion mri","year":2023,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Advanced Neuroimaging Techniques and Applications","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Equivariant map; Convolutional neural network; Gauge (firearms); Diffusion MRI; Diffusion; Physics; Computer science; Quantum electrodynamics; Statistical physics; Mathematics; Artificial intelligence; Geography; Pure mathematics; Medicine; Magnetic resonance imaging; Quantum mechanics; Radiology","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":[0.0006229946,0.0006898814,0.0004340107,0.0004120199,0.0001706605,0.000408082,0.000720701,0.0005927133,0.00122399],"category_scores_gemma":[0.002266506,0.0002820674,0.0004075825,0.0004907351,0.0004946197,0.0005328275,0.0006787879,0.001031226,0.0004178283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009088888,"about_ca_system_score_gemma":0.0006825829,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009625808,"about_ca_topic_score_gemma":0.01220018,"domain_scores_codex":[0.9998145,0.0000472587,0.000009807281,0.00004781923,0.00005110654,0.00002961958],"domain_scores_gemma":[0.9995919,0.0001711667,0.00005081622,0.00006785335,0.00009235899,0.00002603014],"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":[0.00006803545,0.00003523552,0.0009092163,0.0000513899,0.00005522877,0.00006488268,0.00003500749,0.8639842,0.006372554,0.02392342,0.002330432,0.1021703],"study_design_scores_gemma":[0.00000120666,0.000004863905,0.00007295862,0.000002096213,0.000002008088,0.000004028214,0.000001215747,0.995589,0.0005964971,0.003418589,0.0003056122,0.000001887064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04180869,0.0008408309,0.9532067,0.0003781374,0.00005997127,0.00002700896,0.0002827508,0.0013852,0.002010803],"genre_scores_gemma":[0.72939,0.0007661898,0.2615547,0.000178186,0.0000703398,0.0000868806,0.0009332327,0.0002352455,0.006785131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009625808,"threshold_uncertainty_score":0.01913959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.060881123169515,"score_gpt":0.3081433741238512,"score_spread":0.2472622509543362,"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."}}