{"id":"W3010680535","doi":"10.48550/arxiv.2003.04377","title":"Automatic segmentation of spinal multiple sclerosis lesions: How to generalize across MRI contrasts?","year":2020,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Segmentation; Bottleneck; Leverage (statistics); Dice; Contrast (vision); Artificial intelligence; Generalization; Computer science; Feature (linguistics); Pattern recognition (psychology); Mathematics; Statistics","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":[0.008269205,0.002181684,0.001920554,0.001584875,0.0006901424,0.003121735,0.002218383,0.00352056,0.001658012],"category_scores_gemma":[0.02807834,0.0009821497,0.002013452,0.001229548,0.001372327,0.003386952,0.001936618,0.003159545,0.002352592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001026784,"about_ca_system_score_gemma":0.001547765,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005760044,"about_ca_topic_score_gemma":0.009194081,"domain_scores_codex":[0.996615,0.001105399,0.0002078281,0.001464293,0.000402573,0.0002050113],"domain_scores_gemma":[0.9916761,0.003976297,0.0008158832,0.002171657,0.001076664,0.0002832881],"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":[0.001251467,0.0002662373,0.01527723,0.00114624,0.001287263,0.0003161532,0.0005580736,0.1259745,0.06229073,0.002856283,0.02043913,0.7683367],"study_design_scores_gemma":[0.0001247358,0.0005317195,0.01247846,0.0002710003,0.0003470298,0.0009498578,0.0002716215,0.9019173,0.04138996,0.03178075,0.009818259,0.0001193564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.32491,0.02044006,0.6042991,0.009694789,0.001344047,0.0005004731,0.004445414,0.02675607,0.007609988],"genre_scores_gemma":[0.7154062,0.003264811,0.2639269,0.002574666,0.0006426784,0.0001888581,0.007318039,0.002706611,0.003971296],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008269205,"threshold_uncertainty_score":0.04373229,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116208143807505,"score_gpt":0.2154235094261767,"score_spread":0.1038026950454261,"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."}}