{"id":"W4410369089","doi":"10.22399/ijcesen.2063","title":"Explainable Multi-Module Semantic Guided Attention Network for Accurate Medical Image Segmentation","year":2025,"lang":"en","type":"article","venue":"International Journal of Computational and Experimental Science and Engineering","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Computer science; Segmentation; Image (mathematics); Artificial intelligence; Image segmentation; Computer vision; Natural language processing; Information retrieval","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":[0.000606314,0.0007933601,0.0006738398,0.0005495038,0.0002867646,0.0005399127,0.001177915,0.001338661,0.002001403],"category_scores_gemma":[0.001888323,0.0004102609,0.0008263675,0.0004843666,0.0005596629,0.00113601,0.001046055,0.001107334,0.000343999],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008733294,"about_ca_system_score_gemma":0.000780231,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003992089,"about_ca_topic_score_gemma":0.005502149,"domain_scores_codex":[0.9997585,0.00007014557,0.00001066214,0.00007288416,0.0000495295,0.00003826536],"domain_scores_gemma":[0.9996442,0.0001855615,0.00004133233,0.00003610471,0.00007077485,0.00002197917],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001983873,0.00007669776,0.001576667,0.0001219628,0.0001288444,0.0002010466,0.0001364492,0.7667737,0.01806412,0.01394925,0.003464069,0.1953088],"study_design_scores_gemma":[0.000003027552,0.00001368121,0.0001159367,0.000003272559,0.000009895401,0.00002307956,0.000003741274,0.9941159,0.001332944,0.004103566,0.000271306,0.000003599211],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02450731,0.0004636175,0.9724606,0.0003456799,0.00003502667,0.00002381912,0.00007561852,0.0009085453,0.001179882],"genre_scores_gemma":[0.8095642,0.0004527507,0.1832445,0.0005859431,0.00009069532,0.0001089585,0.0004139959,0.0002332168,0.005305781],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003992089,"threshold_uncertainty_score":0.00793767,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01060228716703951,"score_gpt":0.341193969391231,"score_spread":0.3305916822241916,"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."}}