{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005442,0.0006051142,0.0008282113,0.003216492,0.00030444,0.0008243353,0.0007735202,0.001061736,0.001934518],"category_scores_gemma":[0.004325058,0.0002509379,0.0007331417,0.00132546,0.0004346017,0.0009850782,0.0006656189,0.0005751714,0.0008254593],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004438632,"about_ca_system_score_gemma":0.0004631774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001516187,"about_ca_topic_score_gemma":0.001389303,"domain_scores_codex":[0.9992437,0.00007212315,0.00006586557,0.0001794056,0.0003315274,0.0001074141],"domain_scores_gemma":[0.9981688,0.0006423782,0.0003175497,0.0001238888,0.0006291215,0.0001181846],"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":[0.001666396,0.0002671234,0.03989998,0.0005618475,0.0002083629,0.001704828,0.0002918718,0.0613526,0.09408493,0.005725829,0.007666282,0.78657],"study_design_scores_gemma":[0.00004062941,0.0002033682,0.01737402,0.00005532543,0.0001115819,0.001338232,0.0001174145,0.9423137,0.02987824,0.006787376,0.001735567,0.00004437674],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.210449,0.001123852,0.7824902,0.0003920092,0.0001601106,0.0002687168,0.0004746289,0.001188111,0.00345339],"genre_scores_gemma":[0.8299209,0.0004832124,0.1674644,0.00009329119,0.0001178195,0.00008714299,0.0005304975,0.00005639369,0.001246293],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003216492,"threshold_uncertainty_score":0.006471634,"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."}}