{"id":"W3184319394","doi":"10.18280/ts.380312","title":"Efficient Multi-Organ Multi-Center Cell Nuclei Segmentation Method Based on Deep Learnable Aggregation Network","year":2021,"lang":"en","type":"article","venue":"Traitement du signal","topic":"AI in cancer detection","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Segmentation; Jaccard index; Computer science; Artificial intelligence; Deep learning; Pattern recognition (psychology); Software; Image segmentation","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006413015,0.0002406829,0.0001882544,0.0001114626,0.000308496,0.0002188677,0.0003450561,0.0000854966,0.0003049597],"category_scores_gemma":[0.00001503487,0.0002519075,0.0001180737,0.0006012164,0.00002329441,0.0001882587,0.00009878196,0.0002094147,0.0001116994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004035011,"about_ca_system_score_gemma":0.0001030637,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002020225,"about_ca_topic_score_gemma":0.00003013966,"domain_scores_codex":[0.9974872,0.0003937466,0.00039045,0.000691415,0.0005990557,0.0004380667],"domain_scores_gemma":[0.998951,0.0001325558,0.0002156389,0.0004054889,0.0001714777,0.0001238282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004002535,0.0009976655,0.0005202033,0.00003525599,0.00002103021,0.00002154467,0.0006055747,0.9319929,0.01517491,0.0001371876,0.0003406134,0.05011304],"study_design_scores_gemma":[0.003400941,0.0001834415,0.001277599,0.00005102712,0.00001777729,0.000004827206,0.00006844373,0.9067514,0.08701729,0.0000240709,0.0009655972,0.0002375927],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01364014,0.00006679035,0.9842343,0.0003916157,0.0007495917,0.0004232724,0.000004315894,0.0002057937,0.0002841993],"genre_scores_gemma":[0.5001787,0.00000463939,0.4983248,0.001038505,0.0001830405,0.00005722819,0.00002629725,0.00002596196,0.0001608008],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4865385,"threshold_uncertainty_score":0.9999933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02026579987946393,"score_gpt":0.2630841070379967,"score_spread":0.2428183071585328,"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."}}