{"id":"W4404354368","doi":"10.2139/ssrn.5007476","title":"Robust Lung Segmentation in Chest X-Ray Images Using Modified U-Net with Deeper Network and Residual Blocks","year":2024,"lang":"en","type":"preprint","venue":"SSRN Electronic Journal","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Saskatchewan; Toronto Metropolitan University","funders":"","keywords":"Residual; Net (polyhedron); Segmentation; Computer science; Lung; X-ray; Artificial intelligence; Medicine; Physics; Internal medicine; Mathematics; Algorithm; Optics","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":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00218087,0.000478639,0.0006558489,0.0004800883,0.0001688885,0.0002603999,0.0001765471,0.0003491466,0.00002338848],"category_scores_gemma":[0.00008466111,0.0004107697,0.0001109604,0.0003608619,0.0001078174,0.00009968036,0.0003229869,0.007015282,0.000002798709],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002999241,"about_ca_system_score_gemma":0.006807832,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006314071,"about_ca_topic_score_gemma":0.001929101,"domain_scores_codex":[0.9959056,0.0001734111,0.0006018647,0.0007126704,0.0005576258,0.002048843],"domain_scores_gemma":[0.9988862,0.0001586776,0.0003170897,0.0003328099,0.0001400864,0.0001651162],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001597607,0.0004163746,0.04937717,0.001683771,0.002529848,0.0008893518,0.002083874,0.9246255,0.001945836,0.001717282,0.005315167,0.00781818],"study_design_scores_gemma":[0.03795073,0.005814597,0.07045199,0.05784498,0.02102132,0.0234252,0.01280794,0.333201,0.002556503,0.4252175,0.001578899,0.008129286],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9452692,0.02922341,0.01336849,0.01061646,0.0004357175,0.0008991914,0.000009410646,0.00009593683,0.00008212309],"genre_scores_gemma":[0.9823418,0.007567377,0.00654953,0.0009915231,0.001872919,0.00004046159,0.00005908629,0.0001549966,0.0004223238],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5914245,"threshold_uncertainty_score":0.9998344,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02429574078979688,"score_gpt":0.2974198897619196,"score_spread":0.2731241489721227,"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."}}