{"id":"W4313530895","doi":"10.1016/j.compmedimag.2022.102161","title":"Synchronous Medical Image Augmentation framework for deep learning-based image segmentation","year":2022,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"AI in cancer detection","field":"Computer Science","cited_by":21,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Focus (optics); Image (mathematics); Image segmentation; Sample (material); Transformation (genetics); Deep learning; Medical imaging; Pattern recognition (psychology); Computer vision","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":[0.0005759674,0.0008182758,0.0008943293,0.0008400865,0.000295313,0.0008510428,0.001717861,0.001029972,0.005287841],"category_scores_gemma":[0.001187632,0.000532767,0.0009788796,0.0009034325,0.0004118141,0.0008522125,0.00139254,0.001346106,0.001693889],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005551071,"about_ca_system_score_gemma":0.001506411,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004048562,"about_ca_topic_score_gemma":0.00748712,"domain_scores_codex":[0.9997049,0.0000525109,0.00001484387,0.00008103309,0.0001005273,0.00004606566],"domain_scores_gemma":[0.9996542,0.00009403408,0.00003884978,0.0000730535,0.0001007234,0.00003916833],"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":[0.00051209,0.0002347484,0.001056522,0.0002441075,0.0001274711,0.0001980666,0.00008853173,0.2251341,0.05579375,0.01652705,0.01058572,0.6894978],"study_design_scores_gemma":[0.000008653054,0.00003750348,0.0001489183,0.000006440697,0.00001363752,0.00005868168,0.000004641649,0.987133,0.007337148,0.003363036,0.001882109,0.000006242055],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006392202,0.0002676446,0.9901142,0.0001401055,0.00005786762,0.0000419265,0.0001650089,0.001649944,0.001171016],"genre_scores_gemma":[0.3223116,0.0007728759,0.6643925,0.0004580577,0.0002445304,0.0002509697,0.001306138,0.0006866945,0.009576625],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005287841,"threshold_uncertainty_score":0.01768965,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008452284627012663,"score_gpt":0.2856487643698948,"score_spread":0.2771964797428822,"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."}}