{"id":"W3049320518","doi":"10.1109/jbhi.2020.3016306","title":"Multi-Receptive-Field CNN for Semantic Segmentation of Medical Images","year":2020,"lang":"en","type":"article","venue":"IEEE Journal of Biomedical and Health Informatics","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":103,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"Higher Education Discipline Innovation Project; Natural Science Foundation of Hunan Province; Hunan Provincial Science and Technology Department; National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Concatenation (mathematics); Convolutional neural network; Segmentation; Image segmentation; Receptive field; Feature (linguistics); Pattern recognition (psychology); Context (archaeology); Subnet; Field (mathematics); Encoder; Scale-space segmentation; Computer vision; Feature extraction; Context model; 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":[0.0005147682,0.0007874945,0.0004972458,0.001055547,0.0001797805,0.0004061255,0.000789308,0.0009331946,0.00232655],"category_scores_gemma":[0.0008544608,0.0003218695,0.0007595406,0.0006685998,0.000253569,0.0007982099,0.0005383806,0.0005397227,0.0007632334],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000682988,"about_ca_system_score_gemma":0.000765281,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00624675,"about_ca_topic_score_gemma":0.00968074,"domain_scores_codex":[0.9997663,0.00003233796,0.00001328063,0.00008406342,0.00005860475,0.00004539459],"domain_scores_gemma":[0.9998676,0.00003519193,0.00002096985,0.0000220166,0.00003913737,0.00001512208],"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":[0.0008300111,0.0002271256,0.004648101,0.0004085751,0.000240424,0.0006013541,0.000107288,0.1724229,0.1401358,0.004458262,0.01238224,0.663538],"study_design_scores_gemma":[0.000026415,0.0001181121,0.003279638,0.00004218734,0.00007476249,0.0005034645,0.00003508628,0.953979,0.03400455,0.003650805,0.004257402,0.00002866686],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.116002,0.005338101,0.8624513,0.0006189605,0.0002217645,0.0001769766,0.001837813,0.006135878,0.007217212],"genre_scores_gemma":[0.719394,0.001607339,0.2690364,0.0005837238,0.0001072712,0.0001060682,0.003460302,0.0002734744,0.005431358],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.00624675,"threshold_uncertainty_score":0.01242077,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07036109749813316,"score_gpt":0.3782722430124854,"score_spread":0.3079111455143522,"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."}}