{"id":"W2106829632","doi":"10.1109/cccrv.2004.1301473","title":"Segmentation of the breast region in mammograms using snakes","year":2004,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Cancer Institute","keywords":"Breast tissue; Segmentation; Computer science; Artificial intelligence; Breast cancer; Mammography; Image segmentation; Right breast; Computer vision; Pattern recognition (psychology); Medicine; Cancer; Internal medicine","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0008888516,0.000533254,0.0006701024,0.002170084,0.0004091172,0.001235834,0.0005813398,0.001156967,0.001374443],"category_scores_gemma":[0.001782108,0.0008657438,0.001098681,0.00139098,0.0007824294,0.001076811,0.0008543574,0.0006570207,0.0008415699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003355593,"about_ca_system_score_gemma":0.0004276212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008463431,"about_ca_topic_score_gemma":0.001152826,"domain_scores_codex":[0.9996604,0.00009539552,0.00003179227,0.00007944404,0.00009760934,0.00003536029],"domain_scores_gemma":[0.999391,0.0003149055,0.00009285562,0.00008345478,0.00008462821,0.00003331037],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003932468,0.00009933691,0.005585632,0.0004697233,0.0001767749,0.001000859,0.001029232,0.1036809,0.4349288,0.01682412,0.002543526,0.4332677],"study_design_scores_gemma":[0.00004983954,0.0002845078,0.01191413,0.0002158026,0.0001253486,0.002886618,0.0004657918,0.8094319,0.1241039,0.03457006,0.01583935,0.0001126509],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05573396,0.0009625657,0.9397621,0.0002189463,0.00003073646,0.0001094308,0.0001055434,0.001372046,0.001704605],"genre_scores_gemma":[0.182921,0.001042249,0.8137214,0.0000941073,0.00002701812,0.00005205661,0.0002480925,0.0003116238,0.001582316],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002170084,"threshold_uncertainty_score":0.00470078,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02129558634827436,"score_gpt":0.2463323379870711,"score_spread":0.2250367516387967,"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."}}