{"id":"W3179650117","doi":"10.18280/ria.350306","title":"Dense Hierarchical CNN – A Unified Approach for Brain Tumor Segmentation","year":2021,"lang":"en","type":"article","venue":"Revue d intelligence artificielle","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Artificial intelligence; Computer science; Pattern recognition (psychology); Scale-space segmentation; Sørensen–Dice coefficient; Cluster analysis; Dice; Market segmentation; Hierarchical clustering; Segmentation-based object categorization; Noise (video); Image segmentation; Image (mathematics); Mathematics; Statistics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.0003802672,0.0005807473,0.0004582611,0.0008346193,0.0002357215,0.0007027183,0.0009949058,0.0006868611,0.001510597],"category_scores_gemma":[0.0006460854,0.0003603959,0.0006404366,0.0007063457,0.0003240643,0.0009923886,0.0008344567,0.0005197788,0.0004490196],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009333889,"about_ca_system_score_gemma":0.0008038156,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009758316,"about_ca_topic_score_gemma":0.01203786,"domain_scores_codex":[0.9997546,0.00002859824,0.00001068986,0.00006205296,0.00009510864,0.00004893667],"domain_scores_gemma":[0.9998429,0.00002801475,0.00002036653,0.00003559762,0.000059051,0.00001413876],"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":[0.0001215156,0.00007399151,0.001518379,0.0001738453,0.0001328773,0.0002417381,0.0001146895,0.3712494,0.06953251,0.02561188,0.004167401,0.5270617],"study_design_scores_gemma":[0.000002303142,0.00003027127,0.0005418839,0.000008680313,0.00001718953,0.0001044506,0.00001426362,0.9858366,0.007308815,0.004134576,0.001991844,0.000009112745],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01246532,0.0005052495,0.9834893,0.000113514,0.00003593104,0.00004646329,0.0001060948,0.0006444951,0.002593642],"genre_scores_gemma":[0.46244,0.00137287,0.5262986,0.0002183675,0.00009599524,0.000125598,0.0006504115,0.000188907,0.008609178],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009758316,"threshold_uncertainty_score":0.01940304,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08199199427522416,"score_gpt":0.3067396829978715,"score_spread":0.2247476887226473,"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."}}