{"id":"W3043576164","doi":"10.1038/s41598-020-68631-w","title":"Histological validation of in vivo assessment of cancer tissue inhomogeneity and automated morphological segmentation enabled by Optical Coherence Elastography","year":2020,"lang":"en","type":"article","venue":"Scientific Reports","topic":"Ultrasound Imaging and Elastography","field":"Medicine","cited_by":90,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Russian Science Foundation; University of Toronto; Russian Foundation for Basic Research","keywords":"Segmentation; In vivo; Elastography; Stiffness; Biomedical engineering; Optical coherence tomography; Gold standard (test); Calibration; Computer science; Artificial intelligence; Pathology; Biology; Medicine; Materials science; Radiology; Ultrasound; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008129284,0.000442299,0.0002163752,0.0008289708,0.0001815126,0.0002831319,0.0002547129,0.0005145312,0.0009448068],"category_scores_gemma":[0.001171422,0.0002799221,0.0001451696,0.0002967202,0.0005356294,0.0002637031,0.0003165814,0.0003844749,0.0002294991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001722464,"about_ca_system_score_gemma":0.000253452,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007523456,"about_ca_topic_score_gemma":0.001060451,"domain_scores_codex":[0.9995804,0.00009251892,0.00002769573,0.00009038916,0.0001626709,0.00004640082],"domain_scores_gemma":[0.9989874,0.000361017,0.0001901201,0.0002072296,0.0002021833,0.00005211254],"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":[0.00004088185,0.00001981991,0.0005330892,0.00004126591,0.000004428075,0.00004280231,0.00003167001,0.0005065047,0.9959604,0.00008636604,0.0000164037,0.002716467],"study_design_scores_gemma":[0.000009448901,0.0002159556,0.01301951,0.000008936652,0.00002063536,0.0004534755,0.00004766976,0.007357048,0.9776454,0.00009632568,0.001114046,0.00001157052],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8374587,0.00156091,0.1578451,0.00008901871,0.00004293528,0.0001337688,0.0002272004,0.000537738,0.00210463],"genre_scores_gemma":[0.898201,0.0007936673,0.09918033,0.00005506958,0.00001985793,0.0001780788,0.0002396808,0.0001000789,0.001232289],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009448068,"threshold_uncertainty_score":0.004299223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01782688523410278,"score_gpt":0.3109872270731666,"score_spread":0.2931603418390638,"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."}}