{"id":"W4317504313","doi":"10.1007/978-981-19-7742-8_12","title":"Medical-Network (Med-Net): A Neural Network for Breast Cancer Segmentation in Ultrasound Image","year":2023,"lang":"en","type":"book-chapter","venue":"Smart innovation, systems and technologies","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"","keywords":"Artificial intelligence; Computer science; Discriminative model; Segmentation; Feature (linguistics); Artificial neural network; Pattern recognition (psychology); Encoder; Breast ultrasound; Salient; Image segmentation; Deep learning; Breast cancer; Computer vision; Cancer; Medicine; Mammography","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":[0.0002770892,0.0006332335,0.0002618251,0.000526951,0.0001367359,0.000441511,0.0006759169,0.000717448,0.01338801],"category_scores_gemma":[0.000627819,0.0002863011,0.0003210831,0.0005276104,0.000195535,0.0007230341,0.0004249341,0.0007567739,0.004676917],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003428336,"about_ca_system_score_gemma":0.0003044652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0027209,"about_ca_topic_score_gemma":0.006444929,"domain_scores_codex":[0.9999118,0.00001011256,0.000004987752,0.00002345822,0.000042643,0.00000695138],"domain_scores_gemma":[0.9998995,0.0000461478,0.000007835216,0.000008571875,0.0000307515,0.000007094057],"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":[0.00008026311,0.00003849976,0.0004130108,0.0002402742,0.0000420263,0.00009463639,0.00002681653,0.03922648,0.007659093,0.01154211,0.09115885,0.8494779],"study_design_scores_gemma":[0.00003157676,0.00009495489,0.001433777,0.0001311263,0.00006571972,0.0006001546,0.00002088565,0.7082906,0.027562,0.02374518,0.2379772,0.00004689437],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006765697,0.007073437,0.9275931,0.001176275,0.0007143227,0.0001004353,0.002470491,0.01297821,0.04112801],"genre_scores_gemma":[0.06305987,0.005512051,0.7759219,0.001050425,0.0004558247,0.0002443623,0.00432711,0.001830766,0.1475978],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01338801,"threshold_uncertainty_score":0.04478735,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02019505095306151,"score_gpt":0.2618720152666792,"score_spread":0.2416769643136177,"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."}}