{"id":"W4416960261","doi":"10.1109/embc58623.2025.11254269","title":"LPD-Net: A Lightweight and Efficient Deep Learning Model for Accurate Colorectal Polyp Segmentation","year":2025,"lang":"en","type":"article","venue":"","topic":"Colorectal Cancer Screening and Detection","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Segmentation; Pointwise; Deep learning; Preprocessor; Image segmentation; Computational complexity theory; Pattern recognition (psychology); Scale-space 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":[0.0004634886,0.0009308135,0.0006129199,0.0005401104,0.0002450302,0.0008321945,0.001758999,0.001085255,0.002841825],"category_scores_gemma":[0.001544959,0.0004895038,0.0006318026,0.0004345874,0.0004114162,0.001033168,0.001148109,0.001340513,0.001171218],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001029245,"about_ca_system_score_gemma":0.001337794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008622768,"about_ca_topic_score_gemma":0.0155204,"domain_scores_codex":[0.9997935,0.00002974759,0.00001290529,0.00006613936,0.00006226172,0.00003561529],"domain_scores_gemma":[0.9997408,0.00009916002,0.00002831464,0.00002993235,0.00007108971,0.00003078389],"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":[0.0005808312,0.0002270808,0.002967961,0.0002247218,0.0001256416,0.0002999989,0.0000533468,0.6355962,0.0160825,0.005360465,0.01929287,0.3191884],"study_design_scores_gemma":[0.00001232104,0.00002638332,0.0001455563,0.000008543357,0.000009971274,0.00003713448,0.000003215188,0.9947726,0.002072743,0.001522187,0.001382881,0.000006350906],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03637175,0.001359167,0.9489095,0.0007307726,0.0001862568,0.0001038221,0.001329329,0.006904384,0.004105086],"genre_scores_gemma":[0.5658681,0.001418285,0.4059974,0.001339219,0.0001407842,0.0003765705,0.006299168,0.0008175225,0.01774297],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008622768,"threshold_uncertainty_score":0.01714516,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01353614910498508,"score_gpt":0.287885807265744,"score_spread":0.2743496581607589,"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."}}