{"id":"W1549854013","doi":"10.1007/978-3-540-30135-6_20","title":"Image Segmentation Adapted for Clinical Settings by Combining Pattern Classification and Level Sets","year":2004,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Medical Image Segmentation Techniques","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Pattern recognition (psychology); Artificial intelligence; Classifier (UML); Segmentation; Image segmentation; 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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001837219,0.0004196436,0.0004797207,0.0003861284,0.0002475836,0.0005827748,0.001436501,0.0003458046,0.00001590253],"category_scores_gemma":[0.0002636882,0.0004125089,0.00009848985,0.0002606649,0.0008136996,0.0008969755,0.0005650719,0.0006307633,0.000009406623],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002859824,"about_ca_system_score_gemma":0.0003682053,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001574591,"about_ca_topic_score_gemma":0.00000812912,"domain_scores_codex":[0.9961647,0.00006639694,0.0009097166,0.001533921,0.0008661721,0.0004590617],"domain_scores_gemma":[0.9972454,0.0008966153,0.0005986986,0.0007112372,0.0003225462,0.0002254937],"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":[0.000004301324,0.00003533073,0.00006786341,0.00006454978,0.000009341831,0.000007995784,0.0004218035,0.00001858319,0.001776493,0.0006350861,0.0004873768,0.9964713],"study_design_scores_gemma":[0.002790763,0.0009813067,0.001350451,0.001317647,0.00004336436,0.00006455245,0.000003410658,0.8486814,0.01999188,0.1225915,0.0005496584,0.001633973],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00006781952,0.000147085,0.9956322,0.002094088,0.0006358232,0.000978417,0.00003803095,0.0002513576,0.0001552054],"genre_scores_gemma":[0.01816596,0.0001230269,0.9768097,0.004534148,0.0001327452,0.00004953423,0.0000915328,0.00003647792,0.00005684618],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9948373,"threshold_uncertainty_score":0.9998327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06571400330680793,"score_gpt":0.3523583820694516,"score_spread":0.2866443787626437,"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."}}