{"id":"W3097966505","doi":"10.21105/joss.02705","title":"DeepReg: a deep learning toolkit for medical image registration","year":2020,"lang":"en","type":"article","venue":"The Journal of Open Source Software","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":44,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Wellcome / EPSRC Centre for Interventional and Surgical Sciences; Wellcome Trust; Royal Academy of Engineering; Engineering and Physical Sciences Research Council; National Institute for Health and Care Research; University College London","keywords":"Deep learning; Computer science; Image registration; Artificial intelligence; Open source; Image (mathematics); Computer vision; Software","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001627006,0.001624525,0.001170754,0.001409659,0.0004969268,0.002002762,0.004812344,0.00171181,0.06559587],"category_scores_gemma":[0.005554182,0.001442862,0.001994544,0.001306972,0.0006340998,0.002222948,0.005548754,0.00319479,0.03730103],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001050249,"about_ca_system_score_gemma":0.002375106,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003719658,"about_ca_topic_score_gemma":0.008221404,"domain_scores_codex":[0.9989969,0.000181954,0.00009409404,0.0001943157,0.0004342607,0.00009840153],"domain_scores_gemma":[0.9989059,0.0003661636,0.00008415408,0.0002874593,0.0002427828,0.000113482],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0004591512,0.0001160825,0.001040784,0.001479438,0.00037542,0.0004735383,0.0002692663,0.03824616,0.01263793,0.02490895,0.5911245,0.3288689],"study_design_scores_gemma":[0.0002504102,0.0001416033,0.001533826,0.0003855359,0.00008305815,0.000959256,0.0000806042,0.475799,0.03431375,0.07288376,0.4133175,0.0002517391],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001468905,0.0006566304,0.7668721,0.0005048845,0.0002893318,0.0002223816,0.01255343,0.2108672,0.006565005],"genre_scores_gemma":[0.0407543,0.001591813,0.7971098,0.001586999,0.000147643,0.001582174,0.05730079,0.07107133,0.02885502],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.06559587,"threshold_uncertainty_score":0.21944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02151280826002728,"score_gpt":0.3270870888348887,"score_spread":0.3055742805748615,"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."}}