{"id":"W2126673703","doi":"10.1109/ccece.2007.222","title":"Mining Brain Tumors and Tracking their Growth Rates","year":2007,"lang":"en","type":"article","venue":"","topic":"Image Retrieval and Classification Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Process (computing); Task (project management); Similarity (geometry); Brain tumor; Tracking (education); Artificial intelligence; Fractal; Magnetic resonance imaging; Data mining; Pattern recognition (psychology); Image (mathematics); Pathology; Radiology; Medicine; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009001456,0.0005121946,0.0007671589,0.00503489,0.0002745269,0.000919096,0.0009843179,0.0007467328,0.0004662217],"category_scores_gemma":[0.006508773,0.0002943428,0.0007458075,0.002260786,0.0002595368,0.0009154079,0.0004330849,0.0004352727,0.0003465107],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004388437,"about_ca_system_score_gemma":0.0005159818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004929401,"about_ca_topic_score_gemma":0.005932337,"domain_scores_codex":[0.999443,0.00008402542,0.00009443944,0.0001457649,0.000174214,0.00005856144],"domain_scores_gemma":[0.9961805,0.001983568,0.0008297621,0.0002704062,0.000619849,0.0001158389],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004703898,0.0003252345,0.3573439,0.0003828038,0.0003055529,0.001156674,0.0002520255,0.2024762,0.01286195,0.00288698,0.004453253,0.4170851],"study_design_scores_gemma":[0.00001560772,0.00009827163,0.0443369,0.00003582612,0.0001043862,0.001244306,0.0002505647,0.9366868,0.01003976,0.004605494,0.002559311,0.00002275484],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7805607,0.00189745,0.2079774,0.001060754,0.00005067075,0.0001401886,0.005502648,0.001176856,0.001633255],"genre_scores_gemma":[0.9181315,0.0009092413,0.07472713,0.00004810896,0.00006512798,0.00009606741,0.005173473,0.00004809698,0.0008012365],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00503489,"threshold_uncertainty_score":0.009801388,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02431625573543874,"score_gpt":0.2734436103561474,"score_spread":0.2491273546207086,"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."}}