{"id":"W4295438603","doi":"10.1109/embc48229.2022.9871071","title":"Automated Cell Phenotyping for Imaging Mass Cytometry","year":2022,"lang":"en","type":"article","venue":"2022 44th Annual International Conference of the IEEE Engineering in Medicine &amp; Biology Society (EMBC)","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Bladder Cancer Canada","keywords":"Mass cytometry; Autoencoder; Computer science; Artificial intelligence; Classifier (UML); Pattern recognition (psychology); Deep learning; Phenotype; Biology","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":[0.000475044,0.0001813499,0.0002319231,0.000111011,0.000102127,0.00000777903,0.0006719523,0.00009762825,0.0001012021],"category_scores_gemma":[0.0001426108,0.0001585823,0.0001720997,0.0002660926,0.0001367349,0.000007960401,0.0001449031,0.0003141475,7.110904e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006378783,"about_ca_system_score_gemma":0.00007710883,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001090604,"about_ca_topic_score_gemma":0.00001703099,"domain_scores_codex":[0.9988508,0.00005603838,0.0003485768,0.0003140523,0.0001656374,0.0002648483],"domain_scores_gemma":[0.9993317,0.00006990416,0.0001431252,0.0002292853,0.0001824193,0.00004362517],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007230345,0.00005469319,0.007979624,0.00004182275,0.00009249358,3.803233e-7,0.0004592576,0.009367033,0.9712866,0.000151715,0.01032434,0.0001697429],"study_design_scores_gemma":[0.008340936,0.000823424,0.004863573,0.0002482103,0.0001531691,0.0000639283,0.004064294,0.5913709,0.09217472,0.0007668174,0.2957433,0.001386761],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.829337,0.0009495959,0.1607566,0.002155428,0.005115117,0.0004789183,0.0004518271,0.0000974077,0.0006581293],"genre_scores_gemma":[0.9952742,0.0001892913,0.002527334,0.0004802019,0.0003376856,0.00006601179,0.0003904881,0.00002570239,0.0007091201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8791119,"threshold_uncertainty_score":0.64668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02412609165528726,"score_gpt":0.2819635297436936,"score_spread":0.2578374380884063,"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."}}