{"id":"W2998004016","doi":"10.1609/aaai.v34i01.5475","title":"OF-MSRN: Optical Flow-Auxiliary Multi-Task Regression Network for Direct Quantitative Measurement, Segmentation and Motion Estimation","year":2020,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Cerebrovascular and Carotid Artery Diseases","field":"Medicine","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"National Natural Science Foundation of China","keywords":"Optical flow; Segmentation; Computer science; Artificial intelligence; Computer vision; Motion estimation; Artifact (error); Pattern recognition (psychology); Image (mathematics)","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":[0.001542529,0.001522119,0.00108611,0.0006782296,0.0003376844,0.0005712054,0.001671214,0.001317083,0.001516934],"category_scores_gemma":[0.003333204,0.000463171,0.0009561076,0.0006590617,0.0004709627,0.001040514,0.0009818624,0.001284249,0.0005402247],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00078062,"about_ca_system_score_gemma":0.000909753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005514313,"about_ca_topic_score_gemma":0.005610209,"domain_scores_codex":[0.9993703,0.0001941126,0.00002450174,0.0002124522,0.0001138427,0.00008469007],"domain_scores_gemma":[0.999149,0.0003987985,0.0001233159,0.00008238329,0.0002000516,0.00004641229],"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":[0.0004061647,0.0002843638,0.002363733,0.0001734588,0.0001864113,0.0001802192,0.0000943941,0.5919576,0.02386156,0.003917753,0.006430321,0.370144],"study_design_scores_gemma":[0.000005937908,0.00003114549,0.0001936313,0.000003818018,0.000009778498,0.00001950307,0.000002962388,0.9973841,0.001183782,0.0007614685,0.0003982403,0.00000571746],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01956636,0.0005452044,0.976716,0.0002427678,0.00008180254,0.00005927528,0.0001514961,0.001405317,0.001231924],"genre_scores_gemma":[0.585676,0.0006348894,0.4032595,0.0006420627,0.0002695936,0.0003246169,0.001231078,0.0003405147,0.007621852],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005514313,"threshold_uncertainty_score":0.01096445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1401995171891156,"score_gpt":0.33605443310716,"score_spread":0.1958549159180444,"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."}}