{"id":"W4318475776","doi":"10.1007/s13755-022-00209-4","title":"MCA-UNet: multi-scale cross co-attentional U-Net for automatic medical image segmentation","year":2023,"lang":"en","type":"article","venue":"Health Information Science and Systems","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"National Natural Science Foundation of China","keywords":"Computer science; Segmentation; Artificial intelligence; Image segmentation; Scale (ratio); Code (set theory); Context (archaeology); Task (project management); Image (mathematics); Computer vision; Pattern recognition (psychology); Cartography","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":[0.0009201295,0.001366884,0.001180666,0.002346115,0.000946758,0.001281574,0.002115233,0.001888126,0.01076234],"category_scores_gemma":[0.001761554,0.0007441847,0.001133069,0.001883968,0.0003574965,0.001249659,0.001930253,0.0009620769,0.002849095],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000991641,"about_ca_system_score_gemma":0.001451388,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.023179,"about_ca_topic_score_gemma":0.04796643,"domain_scores_codex":[0.9996791,0.00004156866,0.00002093707,0.0001084694,0.00008629696,0.00006364972],"domain_scores_gemma":[0.9995474,0.0001479001,0.00003456557,0.0000899846,0.0001356177,0.00004450616],"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":[0.0008242753,0.0002388804,0.001861432,0.0002652967,0.0002865384,0.0002312459,0.00008924324,0.03102645,0.01859783,0.003787387,0.03902249,0.903769],"study_design_scores_gemma":[0.00004076109,0.00008726504,0.0009179485,0.00002948187,0.00005710224,0.0001678448,0.00003084536,0.9708558,0.01665521,0.003804343,0.007323729,0.00002969658],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02329317,0.001474496,0.9218365,0.00027444,0.0002855717,0.0003172902,0.002836863,0.04590375,0.003777938],"genre_scores_gemma":[0.1414965,0.000513328,0.8418379,0.0005002415,0.0001151596,0.000400493,0.005258454,0.001758601,0.008119388],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.023179,"threshold_uncertainty_score":0.04608816,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08608051872876397,"score_gpt":0.4649126901722104,"score_spread":0.3788321714434464,"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."}}