{"id":"W2131546334","doi":"10.1109/isbi.2010.5490162","title":"Wavelet-based variational deformable registration for ultrasound","year":2010,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"","keywords":"Image registration; Artificial intelligence; Computer vision; Computer science; Wavelet; Discrete wavelet transform; Pyramid (geometry); Energy (signal processing); Wavelet transform; Multiresolution analysis; Pattern recognition (psychology); Image (mathematics); 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":[0.001303166,0.0005422134,0.0008304208,0.0008166218,0.0002746298,0.000539903,0.001208365,0.001105953,0.001254639],"category_scores_gemma":[0.002774425,0.0006300314,0.001103309,0.0009983106,0.0009585653,0.0008286234,0.001149927,0.001202412,0.0004965322],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007687312,"about_ca_system_score_gemma":0.0007544635,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003493695,"about_ca_topic_score_gemma":0.002902432,"domain_scores_codex":[0.9995715,0.0001466239,0.00002641099,0.00007572567,0.0001580623,0.00002172366],"domain_scores_gemma":[0.9995711,0.0002213928,0.00006003242,0.00006876984,0.00006046987,0.0000182542],"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":[0.00005383117,0.00002692715,0.0002508344,0.0001272603,0.00007550992,0.0001154045,0.00009789394,0.7939559,0.02335336,0.093248,0.001283024,0.08741213],"study_design_scores_gemma":[0.000004288316,0.000009854228,0.00007009114,0.000003433402,0.000004554404,0.00002808742,0.000003816123,0.9864681,0.001190576,0.01100975,0.001199135,0.000008299983],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001965075,0.0001522784,0.9974044,0.0000745888,0.00001166463,0.00001361059,0.0000154171,0.00008807814,0.000274893],"genre_scores_gemma":[0.1873984,0.001205189,0.8040797,0.0001251939,0.00008085549,0.0002491491,0.0003141926,0.0004505476,0.006096717],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003493695,"threshold_uncertainty_score":0.006946743,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01225563990767804,"score_gpt":0.2748404721255818,"score_spread":0.2625848322179038,"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."}}