{"id":"W4294975678","doi":"10.1109/iri54793.2022.00068","title":"ARSeg: An Attention RegSeg Architecture for CXR Lung Segmentation","year":2022,"lang":"en","type":"article","venue":"","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Moncton","funders":"","keywords":"Segmentation; Lung; Computer science; Artificial intelligence; Task (project management); Lung disease; Disease; Pattern recognition (psychology); Pathology; Medicine; Internal medicine","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":[0.0005039143,0.001297863,0.0009285348,0.000994612,0.0004090397,0.0006542385,0.00186401,0.001393396,0.004751981],"category_scores_gemma":[0.0007818051,0.0004137119,0.001148571,0.0006812039,0.0002984164,0.000893943,0.0009568266,0.001093899,0.002324236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007632864,"about_ca_system_score_gemma":0.0009961189,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0141447,"about_ca_topic_score_gemma":0.02011927,"domain_scores_codex":[0.9998072,0.00002149973,0.00001104719,0.00008045467,0.00004396493,0.00003583242],"domain_scores_gemma":[0.9998598,0.00003967628,0.00001144705,0.0000228819,0.00005336659,0.00001272872],"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":[0.0007012153,0.000226813,0.003113622,0.0001882148,0.0002529686,0.0003262585,0.00009472945,0.1022659,0.03824893,0.002062644,0.0179894,0.8345293],"study_design_scores_gemma":[0.00003648328,0.0002227864,0.00201114,0.00003159575,0.0001185417,0.0002608035,0.00002901802,0.969729,0.01761742,0.002081016,0.007829839,0.00003230353],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06996808,0.005538743,0.8782855,0.0007577313,0.000409944,0.0002622751,0.002042601,0.03652452,0.006210731],"genre_scores_gemma":[0.5369677,0.002115607,0.4286947,0.001363378,0.0002967574,0.0003253907,0.007764467,0.0009854588,0.02148648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0141447,"threshold_uncertainty_score":0.02812475,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02876508515180147,"score_gpt":0.3490294268555168,"score_spread":0.3202643417037153,"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."}}