{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008903914,0.0002876381,0.0003775886,0.0005523213,0.0008100237,0.000256773,0.00007537596,0.000129532,0.00003486517],"category_scores_gemma":[0.0007683136,0.0002998649,0.0001117841,0.0003064027,0.0001181897,0.00031813,0.00013754,0.0003436638,0.00001769621],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001265607,"about_ca_system_score_gemma":0.00008944182,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001302238,"about_ca_topic_score_gemma":0.000008088881,"domain_scores_codex":[0.9977639,0.0001809006,0.0005013981,0.0005405459,0.0003919695,0.0006212984],"domain_scores_gemma":[0.9985957,0.0006054501,0.0001280234,0.0001326982,0.0003979954,0.0001401923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004783425,0.0002202686,0.4586752,0.00201838,0.0004790313,0.00002158333,0.01798839,0.00132415,0.4933066,0.0001554935,0.02271333,0.00304969],"study_design_scores_gemma":[0.002265839,0.0002693312,0.33774,0.0004598332,0.00009878186,0.00001565789,0.02323367,0.6342957,0.0007915125,0.0001357182,0.0006449196,0.00004907676],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9026859,0.0001214686,0.09005301,0.0004619946,0.00007525938,0.001321769,0.00001190322,0.0004734337,0.004795255],"genre_scores_gemma":[0.9655498,0.00001381075,0.002467594,0.001872852,0.0001294541,0.0003563735,0.0004259171,0.00005300972,0.02913117],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6329715,"threshold_uncertainty_score":0.9999453,"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."}}