{"id":"W2672984453","doi":"10.1109/isbi.2017.7950487","title":"Multiple instance learning for age-related macular degeneration diagnosis in optical coherence tomography images","year":2017,"lang":"en","type":"article","venue":"","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia; Simon Fraser University","funders":"","keywords":"Macular degeneration; Optical coherence tomography; Artificial intelligence; Computer science; Retinal pigment epithelium; Computer vision; Feature (linguistics); Feature extraction; Tomography; Pattern recognition (psychology); Retinal; Ophthalmology; Medicine; Radiology","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":[0.002910134,0.001288564,0.001844081,0.001833925,0.0005679173,0.001253436,0.001930395,0.002495968,0.00115985],"category_scores_gemma":[0.005817609,0.000375791,0.001333409,0.00131005,0.0004235445,0.001525093,0.0007559068,0.002111163,0.0004085871],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001156847,"about_ca_system_score_gemma":0.0009510652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007720828,"about_ca_topic_score_gemma":0.004631696,"domain_scores_codex":[0.9987748,0.0004457728,0.0001023288,0.0003535372,0.0001903418,0.0001331346],"domain_scores_gemma":[0.9973989,0.001794237,0.0002375723,0.0001476537,0.0003005485,0.0001211636],"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":[0.0004234143,0.0006081932,0.008270635,0.0001957279,0.0002672287,0.0002677898,0.00007887508,0.5578182,0.002531074,0.001704389,0.005177934,0.4226565],"study_design_scores_gemma":[0.000008627183,0.00002762405,0.0003686927,0.000006569257,0.00001635234,0.00003650846,0.00001050071,0.9975489,0.000641919,0.001153141,0.0001759062,0.000005336726],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1610248,0.004595751,0.8259665,0.001361912,0.0001780206,0.0002571044,0.0008101113,0.004504758,0.001300973],"genre_scores_gemma":[0.7729492,0.0007756159,0.2223233,0.0003873963,0.0001867978,0.0001906674,0.001841075,0.0001170146,0.001228929],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007720828,"threshold_uncertainty_score":0.01539046,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02133295418935013,"score_gpt":0.3031739251442421,"score_spread":0.281840970954892,"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."}}