{"id":"W4387211538","doi":"10.1007/978-3-031-43901-8_14","title":"M-GenSeg: Domain Adaptation for Target Modality Tumor Segmentation with Annotation-Efficient Supervision","year":2023,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Centre Hospitalier de l’Université de Montréal; Polytechnique Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Segmentation; Modality (human–computer interaction); Leverage (statistics); Annotation; Modalities; Pattern recognition (psychology); Deep learning; Image segmentation; Machine learning","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":[0.001392722,0.002094398,0.002056128,0.001308107,0.0006649566,0.001372459,0.002947467,0.002711689,0.007740927],"category_scores_gemma":[0.002300801,0.0009520077,0.001974107,0.001637513,0.0006263332,0.001311327,0.003225932,0.002812551,0.007730964],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006978242,"about_ca_system_score_gemma":0.00153001,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006767732,"about_ca_topic_score_gemma":0.01250263,"domain_scores_codex":[0.9990968,0.0001535105,0.00003712814,0.0003971797,0.0002215713,0.00009383397],"domain_scores_gemma":[0.9993927,0.0002017525,0.00003391408,0.0001970932,0.0001376878,0.00003669743],"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":[0.0005960768,0.0001570113,0.0005762264,0.0002395161,0.0002303832,0.0002019547,0.0001109725,0.05435049,0.03212219,0.002816555,0.05239679,0.8562018],"study_design_scores_gemma":[0.00007162137,0.0001362192,0.001043209,0.00004655099,0.0001010554,0.000435402,0.00007759989,0.9189906,0.04421558,0.01352281,0.0212958,0.00006351434],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007672423,0.0009419709,0.9493712,0.0002236487,0.0002094522,0.000115754,0.001593342,0.03776865,0.002103439],"genre_scores_gemma":[0.0775032,0.0005265467,0.9004581,0.0004161313,0.000150479,0.0002829465,0.008074952,0.00432455,0.008263098],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007740927,"threshold_uncertainty_score":0.02589595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02515743701589646,"score_gpt":0.2659373605075965,"score_spread":0.2407799234917001,"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."}}