{"id":"W3094554852","doi":"10.21105/joss.02868","title":"ivadomed: A Medical Imaging Deep Learning Toolbox","year":2021,"lang":"en","type":"preprint","venue":"The Journal of Open Source Software","topic":"Medical Imaging and Analysis","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal; Mila - Quebec Artificial Intelligence Institute","funders":"Institut de Valorisation des Données","keywords":"Computer science; Deep learning; Python (programming language); Artificial intelligence; Metadata; Segmentation; Documentation; Toolbox; Machine learning; Modular design; JSON; Medical imaging; Information retrieval; Programming language; World Wide Web","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":[0.001055718,0.001170186,0.0008322722,0.001058494,0.000283944,0.00170542,0.003005674,0.001150624,0.08552136],"category_scores_gemma":[0.00495005,0.001010598,0.001062834,0.0007476296,0.0004202129,0.00124983,0.002763891,0.002614771,0.04416605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006307713,"about_ca_system_score_gemma":0.001627359,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001714252,"about_ca_topic_score_gemma":0.003631512,"domain_scores_codex":[0.9995618,0.00007694355,0.00004443568,0.00009841146,0.0001812762,0.00003708554],"domain_scores_gemma":[0.9991073,0.0004004522,0.00006801581,0.000151346,0.0001852879,0.00008751094],"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":[0.0003152026,0.00007320272,0.001143982,0.001396476,0.0001848742,0.0002622829,0.0001334433,0.02409975,0.007630624,0.01743701,0.7295463,0.2177769],"study_design_scores_gemma":[0.000447049,0.0001012956,0.002315477,0.0004950891,0.00007437417,0.001370802,0.00006088762,0.3959113,0.02333335,0.08546852,0.4902495,0.0001722491],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"software","genre_scores_codex":[0.001743961,0.0005907791,0.7468758,0.0007065217,0.0001939799,0.0002356087,0.02746087,0.2120515,0.01014102],"genre_scores_gemma":[0.04345955,0.001400717,0.7660887,0.00251987,0.0001826002,0.002050248,0.06287643,0.09805077,0.02337107],"genre_candidate":"software","genre_consensus":null,"teacher_disagreement_score":0.08552136,"threshold_uncertainty_score":0.2860975,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01170440096387039,"score_gpt":0.26862667495251,"score_spread":0.2569222739886396,"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."}}