{"id":"W3031116957","doi":"10.3390/jimaging6060052","title":"Explainable Deep Learning Models in Medical Image Analysis","year":2020,"lang":"en","type":"preprint","venue":"Journal of Imaging","topic":"Explainable Artificial Intelligence (XAI)","field":"Computer Science","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Compute Canada; Nvidia","keywords":"Deep learning; Software deployment; Black box; Variety (cybernetics); Computer science; Taxonomy (biology); Data science; Artificial intelligence; Clinical Practice; Medical imaging; Medicine; Software engineering; Biology","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":[0.002208667,0.0005442301,0.000566109,0.001124771,0.0002557274,0.001432609,0.000897548,0.001665171,0.001790556],"category_scores_gemma":[0.007271371,0.0005107117,0.0007084254,0.0008660401,0.001879009,0.002006479,0.001618451,0.002371284,0.0002886147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001314422,"about_ca_system_score_gemma":0.0007498804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003013624,"about_ca_topic_score_gemma":0.002409114,"domain_scores_codex":[0.9991884,0.0004097183,0.00004611947,0.0001213942,0.0001891358,0.00004526619],"domain_scores_gemma":[0.9966105,0.002628311,0.000255023,0.0002570624,0.000187903,0.00006111616],"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":[0.00008176437,0.00004500209,0.002074888,0.0004792774,0.000158651,0.0002025997,0.0002871822,0.4907283,0.002139581,0.3273976,0.006588892,0.1698163],"study_design_scores_gemma":[0.000009051581,0.00002128328,0.0005143087,0.0001155243,0.00001744748,0.00006507717,0.00001928981,0.6931706,0.001039334,0.2991654,0.005840494,0.00002222273],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0108988,0.008460828,0.9729546,0.004408508,0.0000923747,0.00003291194,0.0002062162,0.0004447396,0.00250104],"genre_scores_gemma":[0.6590679,0.01917469,0.3121108,0.001085314,0.0006105105,0.0002023847,0.0007224847,0.0002196977,0.006806211],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003013624,"threshold_uncertainty_score":0.01168066,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03677458504116512,"score_gpt":0.3054511353584473,"score_spread":0.2686765503172822,"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."}}