{"id":"W1557471985","doi":"","title":"Multiple Sclerosis Lesions Segmentation using Spectral Gradient and Graph Cuts","year":2008,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Montreal Neurological Institute and Hospital","funders":"","keywords":"Segmentation; Artificial intelligence; Graph; Computer science; Pattern recognition (psychology); Image segmentation; Market segmentation; Theoretical computer science","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":[0.001549366,0.0001819955,0.0001791088,0.0002166374,0.0006428559,0.000202265,0.000674183,0.00007333668,0.00003765012],"category_scores_gemma":[0.0005106865,0.0001891197,0.00008099312,0.000608643,0.0003104782,0.0006315485,0.000372728,0.000182024,0.0000119565],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008318423,"about_ca_system_score_gemma":0.0000916968,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005902763,"about_ca_topic_score_gemma":0.0001861632,"domain_scores_codex":[0.9968555,0.001523674,0.0003638159,0.0005254267,0.0004294812,0.0003021104],"domain_scores_gemma":[0.9975364,0.0006137859,0.0002103446,0.0008485377,0.0005460669,0.0002448919],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001581804,0.001454046,0.03592187,0.00009373738,0.0000964643,0.00004772356,0.04639536,0.0000550009,0.5310807,0.06329495,0.003536857,0.3180074],"study_design_scores_gemma":[0.001120658,0.000002038445,0.03517065,0.0004565875,0.00002122326,0.0001175829,0.00015505,0.07233869,0.8865426,0.00325579,0.0003396712,0.0004794249],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2699936,0.0001843585,0.7268315,0.001342658,0.00006452463,0.0002251522,0.00000493799,0.0002798635,0.00107339],"genre_scores_gemma":[0.5036026,0.0004193962,0.4953648,0.0001532514,0.000007285863,0.00002018065,0.00002207855,0.00001226548,0.0003982104],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3554619,"threshold_uncertainty_score":0.7712076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04096732039371249,"score_gpt":0.245849297138442,"score_spread":0.2048819767447295,"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."}}