{"id":"W4414398164","doi":"10.33137/codex.v1i1.45681","title":"Multi-Modal Deep Learning for Retinal Analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Computing Data and Exploration","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Deep learning; Retinal; Scalability; Deep neural networks; Pattern recognition (psychology)","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":[0.0005607671,0.000610636,0.0003689472,0.0007990847,0.000269074,0.0008245203,0.000923346,0.0007029179,0.002699933],"category_scores_gemma":[0.001597004,0.0002725306,0.0006280635,0.0005982527,0.0003306599,0.0007770538,0.001141484,0.001015615,0.0006759629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007975089,"about_ca_system_score_gemma":0.0007208061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005412334,"about_ca_topic_score_gemma":0.008359556,"domain_scores_codex":[0.9997415,0.00004788178,0.00001473099,0.00008659263,0.0000703483,0.00003896701],"domain_scores_gemma":[0.999607,0.0001434481,0.00005105604,0.00007116227,0.00009790282,0.00002947593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002612948,0.0002506835,0.003301059,0.0001782775,0.000181006,0.0002267421,0.0001042432,0.2560934,0.02980346,0.01226539,0.01450773,0.6828268],"study_design_scores_gemma":[0.000003439397,0.00001516973,0.000321159,0.000007348177,0.0000100948,0.00002714721,0.000007181558,0.9878201,0.003939054,0.006740852,0.001102409,0.000005945652],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01773989,0.0008806597,0.9753925,0.0004650205,0.00005535824,0.00003918343,0.000397839,0.003548608,0.001480817],"genre_scores_gemma":[0.640963,0.0007368737,0.3505448,0.0005897834,0.0001038778,0.0001168172,0.001229011,0.0002540071,0.005461752],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005412334,"threshold_uncertainty_score":0.01076168,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05460751979904619,"score_gpt":0.3728975629768393,"score_spread":0.3182900431777931,"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."}}