{"id":"W2163640832","doi":"10.1117/1.1579017","title":"Segmentation of breast tumors in mammograms using fuzzy sets","year":2003,"lang":"en","type":"article","venue":"Journal of Electronic Imaging","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Universidade de São Paulo; Conselho Nacional de Desenvolvimento Científico e Tecnológico; University of Calgary","keywords":"Computer science; Cover (algebra); Medical imaging; Segmentation; Image segmentation; Breast imaging; Multimedia; Computer vision; Artificial intelligence; Medical physics; Data science; Mammography; Breast cancer; Medicine; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001173428,0.0000973302,0.0002036346,0.0003569837,0.00002968688,0.00005321105,0.0003397339,0.0000197227,0.00001831261],"category_scores_gemma":[0.00006449775,0.00009084772,0.00007116442,0.0005199898,0.00004269154,0.0009117306,0.00003034643,0.0002877025,7.879657e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003136792,"about_ca_system_score_gemma":0.0003908131,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002633526,"about_ca_topic_score_gemma":0.000003219374,"domain_scores_codex":[0.9983572,0.0001676829,0.0006106127,0.0001247911,0.0004127667,0.000326895],"domain_scores_gemma":[0.999036,0.00004971155,0.0005463722,0.0001434576,0.0001562923,0.00006814302],"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":[0.00004075231,0.0004647915,0.02843079,0.00009634496,0.00007378472,0.0002756032,0.001980144,0.0009391116,0.5345672,0.01036367,0.0003850613,0.4223828],"study_design_scores_gemma":[0.003774433,0.0004439519,0.006464371,0.0008194415,0.00005723186,0.009423421,0.001036136,0.04562853,0.8922818,0.039342,0.0001266359,0.0006019985],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1184206,0.0004503408,0.8804642,0.0002268468,0.00009775106,0.00009312812,2.84729e-7,0.00001809328,0.0002287462],"genre_scores_gemma":[0.8448663,0.00002897874,0.1549271,0.0001468856,0.00001544614,0.000001011793,3.404828e-7,0.00000759417,0.000006327509],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7264457,"threshold_uncertainty_score":0.3704663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009355169760470638,"score_gpt":0.2853766857514503,"score_spread":0.2760215159909797,"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."}}