{"id":"W4401200832","doi":"10.1007/s10278-024-01171-1","title":"AutoCorNN: An Unsupervised Physics-Aware Deep Learning Model for Geometric Distortion Correction of Brain MRI Images Towards MR-Only Stereotactic Radiosurgery","year":2024,"lang":"en","type":"article","venue":"Journal of Imaging Informatics in Medicine","topic":"Advanced Radiotherapy Techniques","field":"Physics and Astronomy","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval; Hôtel-Dieu de Québec; Centre hospitalier de l'Université Laval","funders":"","keywords":"Radiosurgery; Artificial intelligence; Mean squared error; Computer science; Ground truth; Convolutional neural network; Nuclear medicine; Pattern recognition (psychology); Computer vision; Mathematics; Medicine; Radiology; Radiation therapy; Statistics","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.001179892,0.0001922079,0.0005019617,0.0007909338,0.00006118447,0.00004606765,0.0001731793,0.00003348201,0.00002844156],"category_scores_gemma":[0.000128411,0.0001564038,0.0001466478,0.0005957665,0.0000933576,0.001525901,0.0000179605,0.0005366578,2.171832e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002189008,"about_ca_system_score_gemma":0.0001731731,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002038208,"about_ca_topic_score_gemma":4.140804e-7,"domain_scores_codex":[0.9981192,0.00004792434,0.001156507,0.00009240762,0.0003650703,0.0002189065],"domain_scores_gemma":[0.9983744,0.0003457483,0.0007551368,0.0001514571,0.0002933015,0.00007993837],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007918665,0.0000948392,0.01046535,0.0004320348,0.0001159474,0.000005895998,0.006944766,0.08866715,0.00089945,0.0001259073,0.001650299,0.8905192],"study_design_scores_gemma":[0.0008167694,0.0002542072,0.0004079442,0.0008532,0.00006667008,0.00004317347,0.001643142,0.9919407,0.001452727,0.001714584,0.0006526353,0.0001542446],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02089277,0.0004191658,0.9772533,0.0003386358,0.0006038495,0.0002526247,0.000006970043,0.0000477156,0.0001849473],"genre_scores_gemma":[0.9574236,0.0001249227,0.04159349,0.0001000457,0.0005858461,0.00001477668,0.00003118271,0.00003990161,0.00008628329],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9365308,"threshold_uncertainty_score":0.6377963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465845588845727,"score_gpt":0.3162499184229396,"score_spread":0.3015914625344824,"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."}}