{"id":"W3121407777","doi":"10.1117/12.2581362","title":"Uncertainty aware and explainable diagnosis of retinal disease","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Nvidia","keywords":"Computer science; Artificial intelligence; Posterior probability; Bayesian probability; Dropout (neural networks); Machine learning; Deep learning; Segmentation; Macular degeneration; Bayes' theorem; Medicine; Ophthalmology","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":[0.002152451,0.0007669368,0.0005511527,0.001271338,0.0003576879,0.001465174,0.0007304125,0.001572589,0.001386243],"category_scores_gemma":[0.01573384,0.0003846951,0.0007539245,0.0005057491,0.000844127,0.001390365,0.001156062,0.001759609,0.0001628816],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001232532,"about_ca_system_score_gemma":0.0008198352,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005690975,"about_ca_topic_score_gemma":0.004090031,"domain_scores_codex":[0.9990342,0.0003804825,0.00005914126,0.0002053027,0.0002216532,0.00009926652],"domain_scores_gemma":[0.9917859,0.00641943,0.0008282228,0.0004921629,0.0003410501,0.000133204],"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":[0.0005262122,0.000126239,0.03452965,0.0002343247,0.0002136813,0.0007331058,0.0004787526,0.8129805,0.005234618,0.02120784,0.003865262,0.1198698],"study_design_scores_gemma":[0.00001519944,0.00003528696,0.004458832,0.00003808051,0.00002415596,0.0001590436,0.00003360164,0.9583728,0.002642312,0.03359671,0.0006057436,0.00001826288],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3626311,0.002581211,0.6247402,0.004456699,0.0001139125,0.0000685043,0.001386773,0.001145727,0.002875803],"genre_scores_gemma":[0.9725432,0.0003870854,0.02576199,0.0001358309,0.00006442674,0.00002037344,0.00056842,0.00003434278,0.0004843884],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005690975,"threshold_uncertainty_score":0.01138341,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03147320254809458,"score_gpt":0.2770002250594177,"score_spread":0.2455270225113232,"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."}}