{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001030076,0.0003868281,0.0005126442,0.0005573377,0.0002584902,0.000272346,0.0006595924,0.0007795419,0.000009055916],"category_scores_gemma":[0.0001231404,0.0003673005,0.0000537719,0.001249256,0.0002219323,0.0004152517,0.0002886302,0.0005174209,0.000007734588],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002835646,"about_ca_system_score_gemma":0.0001940007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002732446,"about_ca_topic_score_gemma":0.0008104815,"domain_scores_codex":[0.9972832,0.00002506652,0.00092543,0.0007564444,0.0005292413,0.0004805857],"domain_scores_gemma":[0.9979895,0.0003113848,0.000645211,0.0005684377,0.0004571564,0.00002834109],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.00002704571,0.00001040052,0.003250952,0.0005648697,0.0001561433,0.00001971647,0.00009733977,0.001361113,0.00005814389,0.7716277,0.05918269,0.1636439],"study_design_scores_gemma":[0.003172603,0.0005951739,0.0128172,0.007891552,0.0001306272,0.0007645518,0.0007486135,0.06476906,0.0001869393,0.7313817,0.1739827,0.003559333],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004599015,0.01961003,0.8326358,0.03729085,0.04631715,0.01557019,0.001295175,0.02037064,0.02231113],"genre_scores_gemma":[0.5214733,0.03465145,0.09117906,0.003148136,0.02169459,0.02472931,0.00189015,0.001751703,0.2994823],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7414567,"threshold_uncertainty_score":0.9998779,"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."}}