{"id":"W4414198753","doi":"10.1109/cvprw67362.2025.00407","title":"LNTransformer: Lung Nodule Transformer for Sparse CT Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Lung Cancer Diagnosis and Treatment","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto","funders":"","keywords":"Segmentation; Pattern recognition (psychology); Lung; Nodule (geology); Pulmonary vessels; Cut; Image segmentation","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":[0.00134901,0.001400858,0.0009390035,0.001508475,0.0003139847,0.001281329,0.00247622,0.001586343,0.005223501],"category_scores_gemma":[0.003262651,0.0006510693,0.001415688,0.0007242399,0.0004591632,0.0009823682,0.001472682,0.001229399,0.003893489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001086797,"about_ca_system_score_gemma":0.00131324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0061653,"about_ca_topic_score_gemma":0.01277608,"domain_scores_codex":[0.9994468,0.0001005989,0.00002986516,0.0001779105,0.0001835744,0.00006118228],"domain_scores_gemma":[0.9994247,0.0002446187,0.00005069597,0.0001357292,0.0001035045,0.00004065506],"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":[0.0009709881,0.0002396404,0.006500924,0.0003874878,0.0003149323,0.0004708898,0.000149831,0.1667833,0.05249179,0.005418748,0.03286438,0.7334071],"study_design_scores_gemma":[0.00004357932,0.0000775376,0.0009242345,0.0000146045,0.00002747905,0.0004141221,0.00001733133,0.9723969,0.01916413,0.002176536,0.004722129,0.0000214734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0298134,0.0007245797,0.9219567,0.000357136,0.0001019781,0.0003231707,0.001728406,0.04286212,0.002132633],"genre_scores_gemma":[0.3432077,0.0006037607,0.633868,0.0006181574,0.0001119181,0.0004622252,0.008869986,0.00319651,0.009061767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0061653,"threshold_uncertainty_score":0.01747441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01219565429298421,"score_gpt":0.3203040885338013,"score_spread":0.3081084342408171,"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."}}