{"id":"W4391306588","doi":"10.1109/ieeeconf58974.2023.10404605","title":"Choroidal Nevi Classification in Fundus Images Using a Patch-Based Deep Learning Approach","year":2023,"lang":"en","type":"article","venue":"","topic":"Retinal and Optic Conditions","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Thompson Rivers University; University of Alberta; University of Calgary","funders":"","keywords":"Computer science; Upsampling; Artificial intelligence; Fundus (uterus); Class (philosophy); Deep learning; Pattern recognition (psychology); Identification (biology); Image (mathematics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002021647,0.00008037189,0.0001366602,0.0002002325,0.00007605701,0.00002142172,0.00003744447,0.00005714855,0.0002010004],"category_scores_gemma":[0.0001121839,0.00006756252,0.00005085459,0.0005735891,0.00004057923,0.00005452047,0.00001560659,0.0002150191,0.00009737379],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005917433,"about_ca_system_score_gemma":0.00005848319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002177268,"about_ca_topic_score_gemma":0.00002321605,"domain_scores_codex":[0.9992526,0.00003991176,0.0001732913,0.0001900785,0.0001511024,0.0001930387],"domain_scores_gemma":[0.9996675,0.00006019971,0.00003649165,0.0001225623,0.00004517673,0.00006806835],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000245672,0.0006722512,0.7023918,0.0004510477,0.00006216584,0.0002095124,0.001052536,0.01238303,0.2017191,0.002658566,0.001196758,0.07695755],"study_design_scores_gemma":[0.0007827429,0.00007155049,0.3157778,0.00004986769,0.00003270709,0.00001775046,0.001226637,0.6806422,0.0005307617,0.00008574792,0.0006805281,0.0001016196],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9542552,0.00004804558,0.01950451,0.001493626,0.00003966482,0.0002692626,0.000001744758,0.0002107344,0.02417718],"genre_scores_gemma":[0.9908633,0.00001210095,0.005765829,0.0001124748,0.00007002755,0.00003004706,0.0001489833,0.00001362309,0.002983593],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6682593,"threshold_uncertainty_score":0.275512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07090153180946744,"score_gpt":0.3215544436619791,"score_spread":0.2506529118525116,"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."}}