{"id":"W2770792541","doi":"10.1186/s12859-017-1903-6","title":"Brain medical image diagnosis based on corners with importance-values","year":2017,"lang":"en","type":"article","venue":"BMC Bioinformatics","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute on Aging; National Institute of Biomedical Imaging and Bioengineering; Canadian Institutes of Health Research; National Institutes of Health; Servier; H. Lundbeck A/S; IXICO; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China; Eisai; Genentech; Northern California Institute for Research and Education; Eli Lilly and Company; U.S. Department of Defense; Alzheimer's Disease Neuroimaging Initiative; Meso Scale Diagnostics; F. Hoffmann-La Roche; University of Southern California; Pfizer; Biogen; BioClinica; Novartis Pharmaceuticals Corporation; Bristol-Myers Squibb; Foundation for the National Institutes of Health","keywords":"Artificial intelligence; Computer science; Classifier (UML); Pattern recognition (psychology); Matching (statistics); Similarity (geometry); Image registration; Image (mathematics); Medical imaging; Computer vision; Medicine; Pathology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008169064,0.0001816388,0.0002022585,0.0001046271,0.0003070248,0.0005510976,0.001809102,0.0001032399,0.0002015777],"category_scores_gemma":[0.001792121,0.0001296026,0.00006025976,0.0001110647,0.0003934863,0.001236883,0.0002165769,0.0002072455,0.0001199928],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005161888,"about_ca_system_score_gemma":0.0003127666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001300204,"about_ca_topic_score_gemma":0.00002056323,"domain_scores_codex":[0.9978766,0.00004269026,0.0004225089,0.0002007636,0.001160465,0.000296935],"domain_scores_gemma":[0.9974378,0.0003768038,0.0004056587,0.001375252,0.0000907421,0.0003137517],"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":[0.00008583112,0.0005939752,0.08997329,0.0007319989,0.00006246418,0.0002663147,0.002221581,0.00007511256,0.000029402,0.01011372,0.4221686,0.4736777],"study_design_scores_gemma":[0.001113169,0.0003507111,0.007378065,0.0002576993,0.000008257528,0.0000131229,0.00008726366,0.984864,0.004267641,0.0003908352,0.000950428,0.000318827],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0004512963,0.000004825026,0.983856,0.003422885,0.0001262329,0.0002746085,0.000005828666,0.0003421761,0.01151619],"genre_scores_gemma":[0.005331072,0.0000163056,0.9857203,0.008667736,0.00004815177,0.00008182886,0.00001247927,0.00001213903,0.0001099907],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9847889,"threshold_uncertainty_score":0.5314247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0231940258374997,"score_gpt":0.3068828850040999,"score_spread":0.2836888591666002,"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."}}