{"id":"W4388586356","doi":"10.1038/s41598-023-47019-6","title":"Predicting OCT biological marker localization from weak annotations","year":2023,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Retinal Imaging and Analysis","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Optical coherence tomography; Diabetic retinopathy; Artificial intelligence; Computer science; Segmentation; Macular degeneration; Pattern recognition (psychology); Medicine; Ophthalmology; Diabetes mellitus","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":[0.0009598123,0.001036085,0.0004804261,0.001276453,0.0002032802,0.0009001674,0.0005833299,0.0008182661,0.0004959603],"category_scores_gemma":[0.003538281,0.0003346765,0.0004961918,0.0004382882,0.0002557215,0.0006488848,0.0006147485,0.0007114771,0.0003406918],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005279809,"about_ca_system_score_gemma":0.0005322034,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005292056,"about_ca_topic_score_gemma":0.007754326,"domain_scores_codex":[0.9997384,0.00007067394,0.00002220898,0.00008097686,0.00005127899,0.00003654002],"domain_scores_gemma":[0.9988368,0.0005165996,0.0002173167,0.0001005678,0.0002766498,0.00005212536],"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":[0.001286132,0.0002852379,0.1224737,0.0003308583,0.0001918862,0.0007693882,0.000153886,0.3969923,0.06479827,0.001578021,0.006948739,0.4041916],"study_design_scores_gemma":[0.000015808,0.00005362441,0.006811006,0.00002713422,0.00003525314,0.0001650814,0.00002599166,0.9777452,0.01331357,0.001025545,0.0007699304,0.00001189048],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6523944,0.002718498,0.3372295,0.0007519048,0.0001128573,0.00008048637,0.001493573,0.003376694,0.001842055],"genre_scores_gemma":[0.9459906,0.0004015353,0.05107031,0.0001528773,0.00004954403,0.0000364096,0.001222353,0.00007863175,0.0009977572],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005292056,"threshold_uncertainty_score":0.01052248,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02991278386853738,"score_gpt":0.3013707026926255,"score_spread":0.2714579188240881,"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."}}