{"id":"W3038573609","doi":"10.1007/978-3-030-59716-0_48","title":"Semi-supervised Training of Optical Flow Convolutional Neural Networks in Ultrasound Elastography","year":2020,"lang":"en","type":"preprint","venue":"Lecture notes in computer science","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Convolutional neural network; Optical flow; Artificial intelligence; Artificial neural network; Displacement (psychology); Elastography; Artifact (error); Deep learning; Pattern recognition (psychology); Ultrasound; Machine learning; Image (mathematics); Acoustics; Physics","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":[0.001582572,0.0008374945,0.001016738,0.0005749768,0.0003393084,0.0007507685,0.001312481,0.001625329,0.001399893],"category_scores_gemma":[0.005086302,0.0007778769,0.0007200375,0.0004810791,0.0007069275,0.001059507,0.001080096,0.001530924,0.0005041574],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006473943,"about_ca_system_score_gemma":0.00144764,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007679234,"about_ca_topic_score_gemma":0.009042835,"domain_scores_codex":[0.9995407,0.0001729052,0.00002572129,0.0001244266,0.00007114296,0.00006508024],"domain_scores_gemma":[0.9976091,0.001463001,0.0001942553,0.0002135423,0.0004316225,0.00008855292],"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":[0.0003801183,0.0001722949,0.001466478,0.0001728264,0.00008505845,0.00007624438,0.00008574986,0.6353851,0.01493207,0.003139238,0.002921665,0.3411831],"study_design_scores_gemma":[0.000002946938,0.00001473837,0.0001465682,0.000004947398,0.000003292702,0.00000910313,0.000002473941,0.9976942,0.001468145,0.0005393703,0.0001117652,0.000002490129],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07456561,0.0008109117,0.9214973,0.0003471909,0.00007750605,0.00006523093,0.0001896597,0.001455207,0.0009912633],"genre_scores_gemma":[0.7776529,0.0005537458,0.2146582,0.0002202039,0.0001169821,0.0001729603,0.0007591796,0.0002257157,0.005640104],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007679234,"threshold_uncertainty_score":0.01526904,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02454818363695207,"score_gpt":0.273193777593021,"score_spread":0.248645593956069,"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."}}