{"id":"W4327521834","doi":"10.1109/tcsii.2023.3257728","title":"A Multi-Scale Channel Attention Network for Prostate Segmentation","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits & Systems II Express Briefs","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Nanyang Technological University","keywords":"Segmentation; Prostate cancer; Prostate biopsy; Prostate; Computer science; Artificial intelligence; Magnetic resonance imaging; Medicine; Pattern recognition (psychology); Radiology; Cancer; Internal medicine","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005769941,0.0009623268,0.000826147,0.0009760402,0.0005412287,0.0006183774,0.001394217,0.001260638,0.002401413],"category_scores_gemma":[0.001193702,0.0004625894,0.001001354,0.0007891926,0.0004493852,0.001079172,0.0009284105,0.001061754,0.0004869052],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001331528,"about_ca_system_score_gemma":0.00106564,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01450264,"about_ca_topic_score_gemma":0.01886913,"domain_scores_codex":[0.9997124,0.00004416188,0.00001135468,0.0001028993,0.00005733981,0.00007181044],"domain_scores_gemma":[0.9996916,0.0001142632,0.00002715014,0.00003647216,0.0001016391,0.00002888429],"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":[0.0003546536,0.0002002863,0.001751749,0.0001101433,0.0001635385,0.0002466651,0.00008943245,0.485828,0.01641731,0.003679327,0.008571068,0.4825878],"study_design_scores_gemma":[0.000005750795,0.000027184,0.000271245,0.000003908342,0.00001736389,0.00003859011,0.000005925117,0.9960693,0.001996141,0.001056575,0.0005015407,0.000006479453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08951785,0.002781498,0.8935248,0.0008971056,0.0004117874,0.0001249231,0.0004320452,0.005700314,0.006609706],"genre_scores_gemma":[0.8407639,0.0008395959,0.1457466,0.0007983702,0.0003060689,0.0001082685,0.001077019,0.0002565463,0.01010364],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01450264,"threshold_uncertainty_score":0.02883649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03580512116646565,"score_gpt":0.2782785661222263,"score_spread":0.2424734449557607,"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."}}