{"id":"W4406642521","doi":"10.1016/j.media.2025.103473","title":"Towards contrast-agnostic soft segmentation of the spinal cord","year":2025,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Mila - Quebec Artificial Intelligence Institute; Polytechnique Montréal","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institute of Neurological Disorders and Stroke; Fonds de recherche du Québec – Nature et technologies; Fonds de Recherche du Québec - Santé; Natural Sciences and Engineering Research Council of Canada; Réseau en Bio-Imagerie du Quebec; Canadian Institutes of Health Research; National Institutes of Health; Canada Research Chairs; Canada First Research Excellence Fund; Boettcher Foundation; Canada Foundation for Innovation","keywords":"Segmentation; Artificial intelligence; Contrast (vision); Computer science; Computer vision; Pattern recognition (psychology); Anatomy; Medicine","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003988671,0.0001292309,0.0004029441,0.0002536202,0.00005942127,0.00002734103,0.0003472597,0.00007656104,0.001004385],"category_scores_gemma":[0.001176989,0.00008775987,0.0003898747,0.002090985,0.0002308878,0.00005684249,0.00005809734,0.0002442157,0.00001495722],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003938997,"about_ca_system_score_gemma":0.00006889881,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001582694,"about_ca_topic_score_gemma":0.00004023615,"domain_scores_codex":[0.9984424,0.00007504597,0.0004281214,0.0001716061,0.0006758046,0.0002070705],"domain_scores_gemma":[0.9993087,0.0001081045,0.00005077689,0.0003074221,0.00008824792,0.0001367705],"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":[0.00003985561,0.0002835044,0.04368855,0.0006365756,0.01140732,0.00008007141,0.0001849432,0.00511098,0.01859823,0.0003770614,0.02021686,0.899376],"study_design_scores_gemma":[0.0009721966,0.00004673055,0.04751734,0.0004144391,0.01107224,0.00000345095,0.0002684955,0.9122224,0.02472448,0.0005964875,0.001823356,0.0003383902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0662982,0.000863811,0.9255177,0.002841559,0.0002674819,0.00008332805,0.00000986608,0.000120419,0.003997668],"genre_scores_gemma":[0.9980671,0.0001002937,0.0008327208,0.0005688206,0.00005266091,0.00001138256,0.00001426301,0.000007660956,0.0003451101],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9317689,"threshold_uncertainty_score":0.9999088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006067156222850755,"score_gpt":0.2776289836562207,"score_spread":0.27156182743337,"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."}}