{"id":"W2750812135","doi":"10.1109/iccvw.2017.143","title":"Texture and Structure Incorporated ScatterNet Hybrid Deep Learning Network (TS-SHDL) for Brain Matter Segmentation","year":2017,"lang":"en","type":"preprint","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Scatternet; Artificial intelligence; Segmentation; Conditional random field; Computer science; Deep learning; Pattern recognition (psychology); Computer vision","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0003676537,0.000591208,0.0004652734,0.0005320309,0.0002028647,0.0004414254,0.0009339805,0.0008313305,0.001412728],"category_scores_gemma":[0.0007016801,0.0002602676,0.0004055063,0.0005233555,0.0004594491,0.0008256847,0.0007572143,0.0006560395,0.0004043758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000682167,"about_ca_system_score_gemma":0.0006788274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005101788,"about_ca_topic_score_gemma":0.01098129,"domain_scores_codex":[0.9998702,0.00002201355,0.000005536045,0.00003941367,0.00003507732,0.00002787026],"domain_scores_gemma":[0.9997967,0.00006793537,0.00002516367,0.00003283922,0.0000515791,0.00002583976],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003921695,0.0002166189,0.003661564,0.0001333478,0.0001431067,0.0002459947,0.0000813476,0.5907615,0.03720897,0.006563928,0.006375789,0.3542158],"study_design_scores_gemma":[0.000008615274,0.00003801868,0.0002770214,0.000004105148,0.000009578951,0.00002644952,0.000006660886,0.992839,0.00448082,0.001723325,0.0005821115,0.000004351966],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1787553,0.0007909624,0.8112988,0.0005312957,0.0001085988,0.00006368217,0.0004884766,0.00386355,0.004099387],"genre_scores_gemma":[0.8118032,0.0003050694,0.1769619,0.000385165,0.00006228686,0.0000764403,0.001396881,0.0001779812,0.008831187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005101788,"threshold_uncertainty_score":0.01014423,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01387985007561634,"score_gpt":0.2865733124992125,"score_spread":0.2726934624235962,"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."}}