{"id":"W4280605118","doi":"10.18280/ts.390242","title":"Image Segmentation with Priority Based Apposite Feature Extraction Model for Detection of Multiple Sclerosis in MR Images Using Deep Learning Technique","year":2022,"lang":"en","type":"article","venue":"Traitement du signal","topic":"Brain Tumor Detection and Classification","field":"Neuroscience","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Artificial intelligence; Computer science; Deep learning; Segmentation; Feature extraction; Pattern recognition (psychology); Feature selection; Image segmentation; Classifier (UML); Magnetic resonance imaging; Mistake; Feature (linguistics); Machine learning; Computer vision; Radiology; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004663827,0.0001313895,0.000125693,0.0002440847,0.0004243789,0.00004019415,0.00008920991,0.00004151081,0.000032244],"category_scores_gemma":[0.00005060876,0.0001399558,0.00005900391,0.0004361534,0.00004761009,0.0003308047,0.0000183364,0.0002821453,3.038033e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003168776,"about_ca_system_score_gemma":0.00004248294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002689851,"about_ca_topic_score_gemma":0.00003098672,"domain_scores_codex":[0.9986431,0.000244372,0.0002461946,0.0003545677,0.0003375183,0.0001742375],"domain_scores_gemma":[0.9993944,0.0001314552,0.0002874159,0.00009550492,0.00005841727,0.00003280241],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000509614,0.0001712654,0.0004236313,0.00004400943,0.000002387779,8.670224e-7,0.0002272618,0.1165039,0.8767316,0.00000773277,0.000001628784,0.005376083],"study_design_scores_gemma":[0.0007403481,0.0001607404,0.002075573,0.00001101874,0.00001107144,0.000005999723,0.0001667028,0.4950649,0.5016646,0.00001323226,0.00000881875,0.00007694203],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3945985,0.000003629602,0.6044294,0.00004783883,0.00002652421,0.0008078839,0.00001782237,0.00005691038,0.0000115155],"genre_scores_gemma":[0.9749658,0.000002100229,0.02415729,0.0000636735,0.00001767357,0.0007299162,0.00001743571,0.00002345621,0.00002271515],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5803673,"threshold_uncertainty_score":0.5707231,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04661240727018493,"score_gpt":0.2705687078688593,"score_spread":0.2239563005986744,"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."}}