{"id":"W4387430259","doi":"10.1007/978-3-031-44858-4_5","title":"VesselShot: Few-shot Learning for Cerebral Blood Vessel Segmentation","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Acute Ischemic Stroke Management","field":"Medicine","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Segmentation; Annotation; Artificial intelligence; Modalities; Task (project management); Deep learning; Sørensen–Dice coefficient; Labeled data; Machine learning; Image segmentation; Pattern recognition (psychology)","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":[0.0006136578,0.001811455,0.001211919,0.001522485,0.0004040764,0.001528883,0.002675178,0.002152944,0.04058964],"category_scores_gemma":[0.001436843,0.001340972,0.001180569,0.001499189,0.0003527775,0.001442236,0.001477536,0.001913106,0.01887329],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000478928,"about_ca_system_score_gemma":0.0007532545,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004697141,"about_ca_topic_score_gemma":0.00892554,"domain_scores_codex":[0.9995916,0.00003909271,0.00001892853,0.0001417048,0.0001613572,0.00004724479],"domain_scores_gemma":[0.9995341,0.0002309129,0.00001882792,0.00007143203,0.0001026882,0.00004196306],"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":[0.0001905188,0.00008339582,0.0001693739,0.0003691717,0.0001070676,0.0001037719,0.0000603456,0.01730046,0.02474559,0.003706666,0.105041,0.8481227],"study_design_scores_gemma":[0.0000828443,0.0001612627,0.001246097,0.0001408575,0.00009729464,0.0007658271,0.00006192528,0.8081095,0.06008335,0.02380565,0.105322,0.0001233888],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002031125,0.001369655,0.9555507,0.0001364501,0.000270012,0.00008616065,0.002027792,0.03467432,0.003853727],"genre_scores_gemma":[0.01933536,0.001690658,0.9425786,0.0002719224,0.0002038268,0.0001889996,0.008120386,0.006516612,0.0210937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.04058964,"threshold_uncertainty_score":0.1357858,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02787085007116944,"score_gpt":0.2807065626317025,"score_spread":0.2528357125605331,"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."}}