{"id":"W3209404053","doi":"10.1109/irce53649.2021.9570898","title":"Breast Ultrasound Image Segmentation Model Based Residual Encoder","year":2021,"lang":"en","type":"article","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Feature extraction; Deep learning; Pattern recognition (psychology); Breast ultrasound; Concatenation (mathematics); CAD; Residual; Convolution (computer science); Image segmentation; Upsampling; Convolutional neural network; Preprocessor; Path (computing); Feature (linguistics); Artificial neural network; Mammography; Algorithm; Image (mathematics); Mathematics","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":[0.0002226302,0.0006255902,0.0004435033,0.000473028,0.0001681641,0.0004550786,0.0009630637,0.0005371768,0.002624738],"category_scores_gemma":[0.0006091746,0.0002567434,0.0004535744,0.0003414713,0.000238597,0.0006579725,0.0003726976,0.0006249305,0.0007670792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007177753,"about_ca_system_score_gemma":0.0009335343,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009723681,"about_ca_topic_score_gemma":0.01202486,"domain_scores_codex":[0.9999106,0.00001028417,0.000005136101,0.00003218119,0.00002635339,0.00001547279],"domain_scores_gemma":[0.9998465,0.0000404235,0.00001867863,0.00001637196,0.00006901759,0.00000904908],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001920699,0.00007900837,0.001002184,0.00007889845,0.00004542819,0.0001221948,0.00003533892,0.7627581,0.01464288,0.008980028,0.003877683,0.2081862],"study_design_scores_gemma":[0.000002907683,0.00001308468,0.00006852594,0.000002411064,0.000006612591,0.00001628291,0.000001031972,0.9969184,0.00192149,0.00063462,0.0004120084,0.000002610216],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04367835,0.0008490824,0.9445973,0.0003481313,0.0001264572,0.00006916213,0.0005451989,0.004271306,0.005514957],"genre_scores_gemma":[0.7690023,0.0007035637,0.2119428,0.0002597737,0.00007537702,0.000155493,0.001290664,0.0001899746,0.01638005],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009723681,"threshold_uncertainty_score":0.0193342,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01243794524017024,"score_gpt":0.2531243563837875,"score_spread":0.2406864111436172,"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."}}