{"id":"W4295917217","doi":"10.1007/978-3-031-16440-8_21","title":"Physically Inspired Constraint for Unsupervised Regularized Ultrasound Elastography","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":6,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Elastography; Computer science; Optical flow; Displacement field; Displacement (psychology); Imaging phantom; Artificial intelligence; Kinematics; Rendering (computer graphics); Radio frequency; Computer vision; Ultrasound; Acoustics; Physics; Optics; Image (mathematics); Finite element method; Telecommunications","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007154687,0.0006922912,0.001188368,0.0004461603,0.0002972279,0.001087642,0.001786654,0.001434267,0.005104255],"category_scores_gemma":[0.002671499,0.0007374205,0.0008361255,0.0008051643,0.001117401,0.001263897,0.002009344,0.002014752,0.001501159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004850412,"about_ca_system_score_gemma":0.0006447075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001319214,"about_ca_topic_score_gemma":0.002266756,"domain_scores_codex":[0.9994816,0.0001569572,0.00002413818,0.0001053879,0.0002046003,0.00002732498],"domain_scores_gemma":[0.9989819,0.0006027578,0.0000796447,0.0001790827,0.0001141482,0.00004235364],"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":[0.0001124613,0.00006322082,0.0001924813,0.0003720761,0.00009107192,0.0001493674,0.00008967247,0.6700326,0.02407105,0.0992333,0.01073971,0.194853],"study_design_scores_gemma":[0.000005238714,0.0000142559,0.0000567781,0.0000134452,0.00000434264,0.00006267346,0.000004043619,0.9754617,0.001633619,0.0186401,0.00409338,0.00001044731],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000802289,0.0001533466,0.9974247,0.0000772245,0.00003294098,0.00001118345,0.00004152273,0.0001757823,0.001280935],"genre_scores_gemma":[0.08484689,0.0007630946,0.8971124,0.000243073,0.0001962725,0.0001730907,0.0005658825,0.0007711095,0.01532826],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005104255,"threshold_uncertainty_score":0.01707542,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01242581611539651,"score_gpt":0.2420482096116571,"score_spread":0.2296223934962606,"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."}}