{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000243895,0.00008925623,0.0001247018,0.0001376769,0.0002002419,0.00001807553,0.00006211426,0.00002782385,0.0007095614],"category_scores_gemma":[0.00005773056,0.0000854885,0.00008295554,0.0001654805,0.00001484973,0.00005971733,0.00004339767,0.0001293093,0.000006556314],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002601744,"about_ca_system_score_gemma":0.00007324146,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007496258,"about_ca_topic_score_gemma":0.00003547978,"domain_scores_codex":[0.9991043,0.00005288895,0.0001552044,0.0002680291,0.000259352,0.0001601874],"domain_scores_gemma":[0.9994842,0.0001000214,0.00005650473,0.000238101,0.00004726174,0.00007391045],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001331482,0.001914536,0.02761296,0.0008525608,0.0002493874,0.00005481526,0.003451192,0.006839228,0.2817991,0.002163487,0.5337854,0.1399458],"study_design_scores_gemma":[0.01440414,0.004772131,0.07787018,0.000186591,0.00105315,0.0002628314,0.003931073,0.05966661,0.02679391,0.002855543,0.807242,0.0009617805],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8303102,0.0001345183,0.05865213,0.1066682,0.0006648449,0.002493729,0.00005238281,0.0004535562,0.000570436],"genre_scores_gemma":[0.91474,0.000008194186,0.02051616,0.05615037,0.0004029983,0.0008577433,0.001032281,0.00006674428,0.006225472],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2734566,"threshold_uncertainty_score":0.77692,"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."}}