{"id":"W4415150354","doi":"10.3390/jimaging11100360","title":"Lung Nodule Malignancy Classification Integrating Deep and Radiomic Features in a Three-Way Attention-Based Fusion Module","year":2025,"lang":"en","type":"article","venue":"Journal of Imaging","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; Health Sciences Centre; University of Toronto; Sunnybrook Health Science Centre; Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada; University of Toronto","keywords":"Pattern recognition (psychology); Deep learning; Convolutional neural network; Encoder; Feature extraction; Malignancy; Adenocarcinoma; Atypical adenomatous hyperplasia","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0008379268,0.001407968,0.001220809,0.00198632,0.0003511077,0.0007828499,0.001448403,0.001128068,0.0009591071],"category_scores_gemma":[0.0008704765,0.0002962278,0.00145772,0.0008268964,0.0003381211,0.0009917789,0.001429717,0.0006633776,0.0004666552],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000821238,"about_ca_system_score_gemma":0.0008635712,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009491897,"about_ca_topic_score_gemma":0.01203873,"domain_scores_codex":[0.9994656,0.00005485672,0.00002396051,0.0002027063,0.0001219236,0.0001309864],"domain_scores_gemma":[0.9996743,0.00007538994,0.00004067875,0.00004292097,0.0001217724,0.00004489319],"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":[0.000625081,0.0007449778,0.02417973,0.0001662067,0.0004696798,0.000345163,0.0001354949,0.09105935,0.07169257,0.001363636,0.003765916,0.8054522],"study_design_scores_gemma":[0.00002121606,0.0002632006,0.009351222,0.00001651982,0.0002128449,0.0002504687,0.00003896809,0.9707685,0.01650168,0.001431193,0.001115263,0.0000288923],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3272643,0.002212242,0.6597461,0.0005134603,0.0001355969,0.0002791217,0.0007971975,0.005655609,0.003396241],"genre_scores_gemma":[0.9000693,0.0003184358,0.09499583,0.0003461943,0.0001078054,0.0001248131,0.00146512,0.00006650067,0.002505983],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009491897,"threshold_uncertainty_score":0.01887327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005775253117694657,"score_gpt":0.2821782505826647,"score_spread":0.27640299746497,"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."}}