{"id":"W3017517361","doi":"10.1007/s11548-020-02152-9","title":"Perioperative margin detection in basal cell carcinoma using a deep learning framework: a feasibility study","year":2020,"lang":"en","type":"article","venue":"International Journal of Computer Assisted Radiology and Surgery","topic":"Nonmelanoma Skin Cancer Studies","field":"Medicine","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"Queen's University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Autoencoder; Margin (machine learning); Artificial intelligence; Deep learning; Computer science; Basal cell carcinoma; Perioperative; Pattern recognition (psychology); Population; Medicine; Radiology; Machine learning; Basal cell; Pathology","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":[0.0006171598,0.0003661044,0.0003957121,0.0002265341,0.0001564255,0.0004821582,0.0004623554,0.0004808896,0.0009746072],"category_scores_gemma":[0.001513167,0.0002259692,0.0003097931,0.0001422998,0.0001869372,0.0005003266,0.0004072249,0.0004707035,0.0002110744],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002305338,"about_ca_system_score_gemma":0.0005137143,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001879686,"about_ca_topic_score_gemma":0.002059967,"domain_scores_codex":[0.9997643,0.00005938009,0.00001414806,0.00005113512,0.00006788058,0.00004305964],"domain_scores_gemma":[0.9994023,0.0002560622,0.00004588749,0.00005269553,0.0001580915,0.0000850057],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.009812396,0.005698032,0.2094539,0.0006084107,0.0003473974,0.002864361,0.0004678826,0.05134238,0.1196688,0.0007875102,0.002331759,0.5966173],"study_design_scores_gemma":[0.000593606,0.01916827,0.1240375,0.0001279685,0.0006192611,0.00488868,0.0007688378,0.7709572,0.07375373,0.001330101,0.003619001,0.0001357952],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9785948,0.0006053533,0.01963352,0.0001277389,0.00003207453,0.00008208631,0.0001148139,0.0001046313,0.0007049013],"genre_scores_gemma":[0.9893234,0.0001807065,0.00991927,0.00005425896,0.0000138728,0.00003367474,0.0001120349,0.00001274235,0.0003499634],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001879686,"threshold_uncertainty_score":0.003737509,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04609469391749999,"score_gpt":0.307850253392523,"score_spread":0.261755559475023,"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."}}