{"id":"W4388999256","doi":"10.1007/978-981-99-8141-0_10","title":"LDW-RS Loss: Label Density-Weighted Loss with Ranking Similarity Regularization for Imbalanced Deep Fetal Brain Age Regression","year":2023,"lang":"en","type":"book-chapter","venue":"Communications in computer and information science","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"","keywords":"Regression; Similarity (geometry); Artificial intelligence; Ranking (information retrieval); Regularization (linguistics); Pattern recognition (psychology); Computer science; Machine learning; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001813139,0.0003296977,0.0003771927,0.001041022,0.001165083,0.001028126,0.002535809,0.0002108719,0.000003604508],"category_scores_gemma":[0.0001451301,0.0003012137,0.00006168253,0.0009510584,0.0009010985,0.005264258,0.001451045,0.0005435986,0.00002568839],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001753166,"about_ca_system_score_gemma":0.0002882446,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005846679,"about_ca_topic_score_gemma":0.00003312003,"domain_scores_codex":[0.9976422,0.00008244243,0.0007666947,0.0004920482,0.0006459191,0.0003707416],"domain_scores_gemma":[0.9961302,0.0005946801,0.0006368917,0.001791639,0.0007065047,0.0001401378],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002864758,0.00002860801,0.0001654931,0.00008263969,0.000017899,0.000004720666,0.003807431,0.00119854,0.00001929313,0.7873126,0.0001920498,0.207142],"study_design_scores_gemma":[0.001290952,0.0001001338,0.001811011,0.0005072902,0.00001077961,0.00003762017,0.00004236751,0.9455783,0.00002031881,0.0225504,0.02757263,0.0004781963],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0001417278,0.00007238612,0.9773163,0.002110076,0.0002804586,0.0007445207,0.00001064964,0.000279121,0.0190448],"genre_scores_gemma":[0.06403749,0.001291926,0.9220009,0.003698705,0.0001119643,0.0001392644,0.000857905,0.00006211285,0.007799753],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9443797,"threshold_uncertainty_score":0.999944,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03795491185278576,"score_gpt":0.2914141955912022,"score_spread":0.2534592837384165,"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."}}