{"id":"W4281263011","doi":"10.1016/j.compbiomed.2022.105571","title":"Eight pruning deep learning models for low storage and high-speed COVID-19 computed tomography lung segmentation and heatmap-based lesion localization: A multicenter study using COVLIAS 2.0","year":2022,"lang":"en","type":"article","venue":"Computers in Biology and Medicine","topic":"COVID-19 diagnosis using AI","field":"Medicine","cited_by":54,"is_retracted":false,"has_abstract":false,"ca_institutions":"AllerGen; Queen's University","funders":"","keywords":"Pruning; Segmentation; Particle swarm optimization; Pattern recognition (psychology); Artificial intelligence; Deep learning; Computer science; Differential evolution; Algorithm; Biology","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":[0.0007737337,0.0002235486,0.0005180523,0.0005715491,0.0005612887,0.00001467129,0.00006426762,0.00008930752,0.00001163032],"category_scores_gemma":[0.0001319801,0.00020364,0.00002768595,0.0003849446,0.0002852409,0.00006808728,0.0001514192,0.0002689908,3.498953e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002505451,"about_ca_system_score_gemma":0.00008602003,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002945581,"about_ca_topic_score_gemma":0.00002751108,"domain_scores_codex":[0.9982151,0.0004277661,0.0003836465,0.0005715989,0.0001509083,0.0002509459],"domain_scores_gemma":[0.9984505,0.0009758301,0.0001597494,0.0001421555,0.00007103007,0.0002007113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001897441,0.0007744192,0.5799983,0.001095329,0.000184595,0.0001439603,0.01393953,0.3921555,0.002502955,0.0001646107,0.0005239482,0.006619507],"study_design_scores_gemma":[0.01606501,0.001969294,0.01172984,0.0002867763,0.0002001048,0.00003069476,0.001567243,0.9673392,0.00003344907,0.0001098864,0.0004879477,0.0001805123],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.531926,0.001568749,0.4619693,0.0030224,0.0002628518,0.001194987,0.000004983467,0.00005011708,5.41537e-7],"genre_scores_gemma":[0.9792291,0.00006156849,0.007443333,0.01281806,0.00009715705,0.00005283363,0.0002730455,0.00002225596,0.000002637883],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5751838,"threshold_uncertainty_score":0.8304196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04998625163877202,"score_gpt":0.3604398528782313,"score_spread":0.3104536012394593,"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."}}