{"id":"W4296962792","doi":"10.1007/978-3-031-16760-7_2","title":"Partial Annotations for the Segmentation of Large Structures with Low Annotation Cost","year":2022,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Children's Hospital of Eastern Ontario; University of Ottawa","funders":"","keywords":"Dice; Annotation; Computer science; Segmentation; Artificial intelligence; Task (project management); Pattern recognition (psychology); Sørensen–Dice coefficient; Image segmentation; Mathematics; Statistics","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":[0.001203495,0.002226714,0.002149697,0.002271965,0.00125138,0.001974547,0.003153784,0.003059024,0.01547229],"category_scores_gemma":[0.004932059,0.001479105,0.001675309,0.002954657,0.001087723,0.00416159,0.003234868,0.002908554,0.01000499],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006958824,"about_ca_system_score_gemma":0.001614324,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003596537,"about_ca_topic_score_gemma":0.00894773,"domain_scores_codex":[0.9987133,0.0002489572,0.00006931388,0.0005407225,0.0002849718,0.000142738],"domain_scores_gemma":[0.996626,0.001252117,0.0001209315,0.001344581,0.0004837972,0.0001725469],"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":[0.0006389871,0.0001571154,0.0004278294,0.0006997881,0.0001560176,0.0003361614,0.0002036292,0.03819416,0.08061878,0.0120968,0.02863239,0.8378384],"study_design_scores_gemma":[0.00007257367,0.0002058933,0.001295984,0.0001287206,0.0001588085,0.0007714012,0.0001925513,0.8151037,0.06497409,0.08538415,0.03162257,0.00008956038],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007737921,0.0009077109,0.9790638,0.0001636737,0.0001304443,0.0001028297,0.001223335,0.007942477,0.002727681],"genre_scores_gemma":[0.1008793,0.00110036,0.8735017,0.0002969393,0.0002261645,0.0002702331,0.01012535,0.004165493,0.009434541],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01547229,"threshold_uncertainty_score":0.05175996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01846269120421448,"score_gpt":0.273551194052744,"score_spread":0.2550885028485295,"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."}}