{"id":"W3207698969","doi":"10.1016/j.compmedimag.2021.101988","title":"LF-UNet – A novel anatomical-aware dual-branch cascaded deep neural network for segmentation of retinal layers and fluid from optical coherence tomography images","year":2021,"lang":"en","type":"article","venue":"Computerized Medical Imaging and Graphics","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"Canadian Institutes of Health Research","keywords":"Optical coherence tomography; Computer science; Artificial intelligence; Convolutional neural network; Segmentation; Computer vision; Network architecture; Pattern recognition (psychology); Deep learning; Artificial neural network; Radiology; Medicine","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":[0.0004623908,0.001229466,0.0009018751,0.0008808584,0.0003713736,0.000749839,0.002143208,0.001432248,0.002521478],"category_scores_gemma":[0.000849032,0.0005344108,0.000858339,0.0005916748,0.0002744869,0.001002304,0.001298372,0.00125113,0.001104042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007553264,"about_ca_system_score_gemma":0.001275518,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01203048,"about_ca_topic_score_gemma":0.02688153,"domain_scores_codex":[0.9997552,0.00002663105,0.00001196079,0.0000733248,0.00008297783,0.00004995656],"domain_scores_gemma":[0.9997773,0.00005628049,0.0000218817,0.00003334486,0.00008103286,0.00003016016],"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":[0.0003899911,0.0002576959,0.002157928,0.0001934051,0.000273506,0.000283821,0.00006901315,0.1150464,0.04108074,0.002440432,0.01539378,0.8224133],"study_design_scores_gemma":[0.0000127707,0.00006073127,0.0004164083,0.00001582476,0.0000303475,0.00009501929,0.000008218,0.9893901,0.007143247,0.0009594724,0.00185433,0.00001368409],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03919232,0.001910457,0.9476079,0.0004315597,0.0002787539,0.0001494888,0.0009502679,0.006172929,0.003306342],"genre_scores_gemma":[0.3930411,0.001420958,0.5801235,0.001045576,0.0002254842,0.0002677904,0.003544364,0.000594525,0.0197367],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01203048,"threshold_uncertainty_score":0.02392089,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01311444127564886,"score_gpt":0.2806330781246108,"score_spread":0.2675186368489619,"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."}}