{"id":"W2080572871","doi":"10.1109/icip.2010.5652047","title":"Mean shift based algorithm for mammographic breast mass detection","year":2010,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Mean-shift; Mammography; Pixel; Artificial intelligence; Computer science; Pattern recognition (psychology); Image segmentation; Feature extraction; Feature (linguistics); Computer vision; Segmentation; Algorithm; Breast cancer; Cancer","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":[0.0005007061,0.0005896076,0.0006376459,0.00119014,0.0004351452,0.0005776483,0.0007639735,0.001036075,0.002519845],"category_scores_gemma":[0.001310694,0.0003025482,0.0005694458,0.001291836,0.0003653048,0.0007924745,0.0005044607,0.0008206915,0.00174414],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004703007,"about_ca_system_score_gemma":0.0006032214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001541236,"about_ca_topic_score_gemma":0.0018579,"domain_scores_codex":[0.9994524,0.00008471165,0.00002537051,0.000100544,0.0003079181,0.00002907956],"domain_scores_gemma":[0.9997322,0.0000834605,0.00002604959,0.00003707053,0.0001104078,0.00001078016],"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":[0.0001999953,0.00005552468,0.00102776,0.0002134296,0.00009885598,0.0001172423,0.00009420282,0.02315517,0.06917301,0.01277499,0.00752051,0.8855693],"study_design_scores_gemma":[0.00007756955,0.0003796293,0.005294655,0.00007195389,0.0001406083,0.001565072,0.00008203086,0.782777,0.09611759,0.02150212,0.0918504,0.0001413797],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006648903,0.002195753,0.9880617,0.0001762669,0.0001698603,0.00005649328,0.0000785697,0.0008980312,0.001714544],"genre_scores_gemma":[0.1005668,0.002274887,0.8890953,0.0001935124,0.0002112786,0.0001681829,0.0003211913,0.0001286757,0.007040247],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.002519845,"threshold_uncertainty_score":0.008429706,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006929554286713935,"score_gpt":0.2201408378948987,"score_spread":0.2132112836081847,"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."}}