{"id":"W2122108494","doi":"10.1109/cvpr.2007.383010","title":"Segmenting Images on the Tensor Manifold","year":2007,"lang":"en","type":"article","venue":"","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Bhattacharyya distance; Intrinsic dimension; Tensor (intrinsic definition); Manifold (fluid mechanics); Artificial intelligence; Image segmentation; Segmentation; Mathematics; Metric (unit); Riemannian manifold; Computer science; Market segmentation; Pattern recognition (psychology); Space (punctuation); Mathematical analysis; Geometry; Curse of dimensionality","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":[0.0009612732,0.00006789847,0.00005032888,0.00005176258,0.0001004045,0.0001214644,0.0005905688,0.00002124699,0.0002794152],"category_scores_gemma":[0.0001081677,0.00003872785,0.00002953157,0.0001770066,0.00002750652,0.0001874913,0.0001341667,0.0001003215,0.000215671],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001917975,"about_ca_system_score_gemma":0.000008180571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000116592,"about_ca_topic_score_gemma":0.000001358695,"domain_scores_codex":[0.9991239,0.00003010025,0.0001536361,0.0001788237,0.0003112587,0.0002023341],"domain_scores_gemma":[0.9992153,0.0002889697,0.00004482252,0.0003558881,0.00003806247,0.00005693878],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000005050294,0.0001347931,0.0006394722,0.00001126745,0.00002081958,0.00009909425,0.0004781254,0.000001077543,0.0754538,0.3287641,0.1659027,0.4284897],"study_design_scores_gemma":[0.000106912,0.00004544685,0.002212109,0.00001279861,0.000001505283,0.000007702319,0.0001109814,0.0008380142,0.9937002,0.001647086,0.001221379,0.00009585187],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00151525,0.000006618833,0.9254345,0.002957096,0.00008022162,0.000121676,1.550366e-7,0.0003977177,0.06948681],"genre_scores_gemma":[0.2563158,0.000007498816,0.7143753,0.02043077,0.0001044324,0.00001360836,6.711621e-7,0.000009771412,0.008742131],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9182464,"threshold_uncertainty_score":0.30594,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0195429106902412,"score_gpt":0.2784481375915804,"score_spread":0.2589052269013392,"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."}}