{"id":"W4404045921","doi":"10.1007/s13246-024-01489-8","title":"Improving deep learning U-Net++ by discrete wavelet and attention gate mechanisms for effective pathological lung segmentation in chest X-ray imaging","year":2024,"lang":"en","type":"article","venue":"Physical and Engineering Sciences in Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Sherbrooke","funders":"","keywords":"Segmentation; Wavelet; Pathological; Deep learning; Artificial intelligence; Lung; Computer science; Net (polyhedron); Pattern recognition (psychology); Medicine; Pathology; Internal medicine; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007355324,0.0001376737,0.0002509469,0.0001838294,0.00006400305,0.00004053966,0.00004041855,0.0000296313,0.000001794958],"category_scores_gemma":[0.0004016297,0.00009982305,0.00002389856,0.0003637126,0.0001564945,0.0001692656,0.00003758611,0.0002311562,3.21859e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007759643,"about_ca_system_score_gemma":0.000009906374,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007692185,"about_ca_topic_score_gemma":0.000002601863,"domain_scores_codex":[0.9989748,0.00002683594,0.0001619095,0.000401288,0.0001880712,0.0002471247],"domain_scores_gemma":[0.9992712,0.0005850198,0.00002468759,0.00004306121,0.00001321888,0.00006281384],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003755885,0.00006706255,0.005532347,0.001703292,0.00001495551,0.00006465612,0.003494686,0.006133813,0.8490979,0.0009192475,0.00005694935,0.1328775],"study_design_scores_gemma":[0.0007532414,0.0003674018,0.02808177,0.001022287,0.00004444971,0.00001103169,0.0003686844,0.9677095,0.001097072,0.0003588764,0.00006759238,0.0001181029],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8526503,0.001273127,0.1406666,0.004703647,0.0001660716,0.000449516,0.000001442659,0.00008170256,0.000007538617],"genre_scores_gemma":[0.9966762,0.00007020547,0.002731427,0.0002607663,0.0001295928,0.000101391,0.00001123494,0.000011876,0.000007292303],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9615757,"threshold_uncertainty_score":0.4070666,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00735277786926795,"score_gpt":0.2930939820406233,"score_spread":0.2857412041713553,"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."}}