{"id":"W4400016384","doi":"10.1016/j.media.2024.103243","title":"CP-Net: Instance-aware part segmentation network for biological cell parsing","year":2024,"lang":"en","type":"article","venue":"Medical Image Analysis","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"CReATe Fertility Centre; University of Toronto","funders":"Temerty Faculty of Medicine, University of Toronto; Natural Sciences and Engineering Research Council of Canada; Vector Institute; Government of Ontario; Ontario Research Foundation","keywords":"Segmentation; Parsing; Computer science; Artificial intelligence; Market segmentation; Image segmentation; Pattern recognition (psychology); Feature (linguistics); Computer vision","routes":{"ca_aff":true,"ca_fund":true,"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":[],"consensus_categories":[],"category_scores_codex":[0.0004261085,0.0001560508,0.000272362,0.0001199177,0.0002096078,0.0001889125,0.0005824179,0.000102142,0.0001672105],"category_scores_gemma":[0.00006617621,0.0001242187,0.0002638756,0.00264399,0.0001065761,0.0003530561,0.0001450654,0.0002004346,0.00006197974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005881579,"about_ca_system_score_gemma":0.00005628548,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004949731,"about_ca_topic_score_gemma":0.00002124878,"domain_scores_codex":[0.9981016,0.0000831435,0.0003730489,0.0006400977,0.000398028,0.0004040604],"domain_scores_gemma":[0.998696,0.0005332747,0.00007196738,0.0004154804,0.00006807361,0.000215172],"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":[0.00003009829,0.0003547511,0.002617155,0.0001561529,0.001212444,0.0002850852,0.0005899661,0.02300312,0.003058491,0.02066295,0.1542196,0.7938101],"study_design_scores_gemma":[0.0001716863,0.00004853684,0.0002008663,0.00003733202,0.000253338,0.000004192929,0.00002552227,0.942751,0.0008889142,0.007447903,0.04793433,0.0002363341],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001618264,0.001059767,0.9929779,0.003256445,0.0002345136,0.0002241936,0.00001183436,0.000321592,0.0002954512],"genre_scores_gemma":[0.5423096,0.001069808,0.449597,0.003532845,0.001663227,0.0005526075,0.0004410147,0.00003554184,0.0007983371],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9197479,"threshold_uncertainty_score":0.5065492,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02439601499852344,"score_gpt":0.3100596373806054,"score_spread":0.2856636223820819,"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."}}