{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002953886,0.0003164852,0.0009482061,0.001463096,0.0001209302,0.000720034,0.00294319,0.0001583146,0.00009434896],"category_scores_gemma":[0.001174651,0.0003092953,0.0005748627,0.001749392,0.00008390537,0.001865125,0.002090504,0.00297751,0.00002629259],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003507994,"about_ca_system_score_gemma":0.0006105789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000257685,"about_ca_topic_score_gemma":0.00005594451,"domain_scores_codex":[0.9955354,0.000409992,0.001448675,0.0005802848,0.001450432,0.0005752643],"domain_scores_gemma":[0.9970639,0.0002994634,0.001112464,0.0005318159,0.0005774641,0.0004148864],"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":[0.00003708735,0.000174622,0.00490303,0.000162646,0.0005487262,0.01156054,0.01077724,0.8881639,0.0006430186,0.007450794,0.0003984131,0.07517999],"study_design_scores_gemma":[0.0001245214,0.00002648593,0.0001784619,0.0002287435,0.0001070522,0.0001144489,0.0009797992,0.9532034,0.0009607088,0.04356788,0.0002353986,0.0002730704],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007414018,0.001423939,0.9792179,0.008984708,0.0004639241,0.00009595099,4.444099e-7,0.0000621744,0.002336869],"genre_scores_gemma":[0.9142097,0.0004025465,0.08448773,0.0005399403,0.0002911247,0.0000057867,0.00000203114,0.00002618333,0.0000349305],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9067957,"threshold_uncertainty_score":0.9999359,"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."}}