{"id":"W4387642557","doi":"10.1007/978-3-031-45673-2_25","title":"Leveraging Self-attention Mechanism in Vision Transformers for Unsupervised Segmentation of Optical Coherence Microscopy White Matter Images","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Cell Image Analysis Techniques","field":"Biochemistry, Genetics and Molecular Biology","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Ground truth; Deep learning; Unsupervised learning; Pattern recognition (psychology); Image segmentation; Computer vision","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":[0.0003050929,0.0004645654,0.0004362512,0.000533114,0.00030686,0.0008126922,0.001059759,0.0005821097,0.004302716],"category_scores_gemma":[0.0007698705,0.0002347823,0.0005200568,0.0005157288,0.000398214,0.001225096,0.000822736,0.0007986634,0.001252229],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005390394,"about_ca_system_score_gemma":0.0004618673,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002133152,"about_ca_topic_score_gemma":0.002811823,"domain_scores_codex":[0.9998552,0.00001718815,0.000008938412,0.0000486054,0.00004089816,0.00002918331],"domain_scores_gemma":[0.9997384,0.00009452864,0.00002172855,0.00004762727,0.00007133417,0.00002634496],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002714866,0.0001918512,0.0004343882,0.000177804,0.00005391111,0.0001497584,0.000108947,0.02327027,0.4535957,0.02292317,0.004271163,0.4945516],"study_design_scores_gemma":[0.00001414272,0.0001016884,0.0007859507,0.00001189707,0.00003304224,0.0002086363,0.00002583103,0.796077,0.1829974,0.01612054,0.003605079,0.00001888473],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0202029,0.0002947556,0.9745445,0.0001006415,0.00006086121,0.00004278883,0.00005464644,0.002265909,0.002433118],"genre_scores_gemma":[0.5780321,0.0005260828,0.4133001,0.0002711306,0.0001012838,0.00007461294,0.0002969789,0.000624804,0.006772989],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004302716,"threshold_uncertainty_score":0.01439399,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00845448943267093,"score_gpt":0.2708489934243807,"score_spread":0.2623945039917098,"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."}}