{"id":"W4409679439","doi":"10.1002/hbm.70212","title":"<scp>WMH</scp> ‐ <scp>DualTasker</scp> : A Weakly Supervised Deep Learning Model for Automated White Matter Hyperintensities Segmentation and Visual Rating Prediction","year":2025,"lang":"en","type":"article","venue":"Human Brain Mapping","topic":"Dementia and Cognitive Impairment Research","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Medical Research Council; Canadian Institutes of Health Research; National Institutes of Health; H. Lundbeck A/S; Genentech; IXICO; National University Health System; Servier; Eisai; Ministry of Education - Singapore; Agency for Science, Technology and Research; Northern California Institute for Research and Education; BioClinica; Biogen; Ministry of Education, India; Pfizer; Novartis Pharmaceuticals Corporation; University of Southern California; U.S. Department of Defense; Eli Lilly and Company; Bristol-Myers Squibb; Alzheimer's Disease Neuroimaging Initiative; Medical Research Council; Meso Scale Diagnostics; Alzheimer's Association","keywords":"Hyperintensity; Segmentation; Artificial intelligence; Clinical Dementia Rating; Neuroimaging; Voxel; Deep learning; Cognitive decline; Rating scale; Pattern recognition (psychology); Cognition; Dementia; Computer science; Psychology; Machine learning; Cognitive impairment; Magnetic resonance imaging; Medicine; Neuroscience; Disease; Developmental psychology; Pathology; Radiology","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009695603,0.001247756,0.0005822831,0.0004822474,0.0002649007,0.0007019757,0.001953327,0.001375709,0.002145151],"category_scores_gemma":[0.00230256,0.0004038229,0.0008401461,0.0003868151,0.0005608692,0.0005980759,0.001419601,0.001664022,0.001168694],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007595221,"about_ca_system_score_gemma":0.001215888,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008944009,"about_ca_topic_score_gemma":0.0124499,"domain_scores_codex":[0.999687,0.00007118432,0.0000136191,0.0001120286,0.00007244079,0.00004379654],"domain_scores_gemma":[0.9995973,0.0001230526,0.00005018994,0.00007257715,0.0001194981,0.00003739334],"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":[0.0004477952,0.000349826,0.005697703,0.0002357379,0.0003355327,0.0003791623,0.0001237148,0.566146,0.01655425,0.004575815,0.04686525,0.3582893],"study_design_scores_gemma":[0.00001497863,0.00004968712,0.0005228563,0.000008761963,0.000009508499,0.00003438799,0.000004232061,0.9943289,0.002220972,0.001594763,0.001200621,0.00001042363],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1338717,0.001045544,0.8372735,0.001701942,0.0002208651,0.0004472773,0.005374635,0.01462599,0.00543856],"genre_scores_gemma":[0.6915251,0.0005873807,0.2720504,0.001421918,0.0001589465,0.0009449403,0.01435346,0.0007647417,0.01819319],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008944009,"threshold_uncertainty_score":0.01778388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02157197237600832,"score_gpt":0.309404806911029,"score_spread":0.2878328345350206,"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."}}