{"id":"W2908300307","doi":"10.1109/tmi.2018.2859478","title":"Transfer Learning for Image Segmentation by Combining Image Weighting and Kernel Learning","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Medical Imaging","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":97,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Institutes of Health Research; National Institutes of Health; Genentech; IXICO; H. Lundbeck A/S; Servier; Eisai; Northern California Institute for Research and Education; F. Hoffmann-La Roche; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; University of Southern California; Biogen; Eli Lilly and Company; Bristol-Myers Squibb; University of California, San Diego; BioClinica; U.S. Department of Defense; Meso Scale Diagnostics; Alzheimer's Disease Neuroimaging Initiative; Novartis Pharmaceuticals Corporation; Pfizer; Alzheimer's Association","keywords":"Artificial intelligence; Kernel (algebra); Computer science; Image segmentation; Weighting; Pattern recognition (psychology); Image (mathematics); Computer vision; Segmentation; Segmentation-based object categorization; Scale-space segmentation; Image texture; Mathematics; Medicine","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":[0.001092208,0.0002592709,0.0002499112,0.0002180349,0.001232151,0.0004351314,0.0003505816,0.0000933454,0.0002103372],"category_scores_gemma":[0.0001299416,0.0002682341,0.00010878,0.0003531487,0.0003185246,0.001372428,0.000007342261,0.0008699853,0.00005861142],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006302839,"about_ca_system_score_gemma":0.00007401645,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002603213,"about_ca_topic_score_gemma":0.000003865108,"domain_scores_codex":[0.9975054,0.0002482295,0.0004375514,0.000633972,0.0006382185,0.0005366848],"domain_scores_gemma":[0.9986242,0.0006131796,0.00008800613,0.0001771573,0.0001642825,0.0003331772],"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":[0.00006355609,0.0001546927,0.0001274001,0.00007746417,0.00006676364,0.00002827208,0.006783203,0.0009979524,0.1558009,0.0007243649,0.000519242,0.8346562],"study_design_scores_gemma":[0.002193549,0.0002204486,0.00003548775,0.0001553855,0.00003399893,0.00006863871,0.001755828,0.9407736,0.04780959,0.000184062,0.006363152,0.00040626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01418244,0.00006503455,0.9812712,0.002266026,0.0005103199,0.000239525,0.000002081475,0.0004669128,0.0009964509],"genre_scores_gemma":[0.9399965,0.00006674786,0.05782159,0.001240227,0.0001244944,0.00006352067,0.000009196255,0.00004928086,0.0006284482],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9397756,"threshold_uncertainty_score":0.999977,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01077902215612788,"score_gpt":0.2725020064135906,"score_spread":0.2617229842574627,"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."}}