{"id":"W4301489718","doi":"10.1007/978-3-031-01651-6_3","title":"Segmentation and Land marking of Mammograms","year":2013,"lang":"en","type":"book-chapter","venue":"Synthesis lectures on biomedical engineering","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Computer science; Segmentation; Computer vision; Artificial intelligence; Cartography; Geography","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":[0.000349343,0.0008684356,0.0008636,0.001691249,0.0003605027,0.001988051,0.001221078,0.0007781057,0.01590831],"category_scores_gemma":[0.001026921,0.0008869662,0.0008121271,0.001899123,0.001038254,0.001563172,0.0009610379,0.00127454,0.009763372],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005495116,"about_ca_system_score_gemma":0.0005693061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001289697,"about_ca_topic_score_gemma":0.002444053,"domain_scores_codex":[0.9997329,0.00002180453,0.00001341983,0.00009067044,0.0001180051,0.00002315329],"domain_scores_gemma":[0.9997305,0.0001146874,0.00001377618,0.00006010416,0.00006226908,0.00001868055],"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":[0.00006749039,0.00002687057,0.0002031878,0.0006162657,0.00002571969,0.000112049,0.0001906918,0.006628931,0.0472079,0.02887886,0.03326225,0.8827798],"study_design_scores_gemma":[0.0000267779,0.0001530959,0.005304046,0.0005077874,0.00008836637,0.002429769,0.0002986215,0.1150113,0.1055982,0.1757409,0.5947067,0.0001345467],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004801566,0.01357671,0.9341512,0.0006682733,0.0007373847,0.0001110032,0.0007590015,0.003819689,0.0413751],"genre_scores_gemma":[0.04864118,0.0200625,0.8047408,0.0003194054,0.0006751847,0.0001054132,0.002128892,0.001631597,0.121695],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01590831,"threshold_uncertainty_score":0.05321854,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006843207094334703,"score_gpt":0.189553678887364,"score_spread":0.1827104717930293,"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."}}