{"id":"W4387346516","doi":"10.1145/3584371.3613050","title":"Deep-Learning Based Cell Segmentation and Deconvolution in Spatial Transcriptomics","year":2023,"lang":"en","type":"article","venue":"","topic":"Single-cell and spatial transcriptomics","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Sanofi (Canada)","funders":"","keywords":"Deconvolution; Computer science; Convolutional neural network; Artificial intelligence; Segmentation; Deep learning; Context (archaeology); Pattern recognition (psychology); Image segmentation; Field (mathematics); Transcriptome; Computational biology; Computer vision; Biology; Gene; Algorithm; Genetics; Mathematics; Gene expression","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":[0.00133876,0.0006576994,0.0009081768,0.0008287833,0.0003830956,0.001194392,0.001625553,0.001642539,0.001267987],"category_scores_gemma":[0.0018589,0.0006228618,0.0008540241,0.001028302,0.00146512,0.001326167,0.001435509,0.001746221,0.000803105],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001807254,"about_ca_system_score_gemma":0.001336408,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008131259,"about_ca_topic_score_gemma":0.009295861,"domain_scores_codex":[0.9996313,0.00007942318,0.0000166933,0.000119782,0.00007625882,0.00007653423],"domain_scores_gemma":[0.9992993,0.000370385,0.00007138427,0.00008781286,0.0001151581,0.00005595102],"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":[0.0003086458,0.0001216755,0.002349878,0.0002741101,0.0001198391,0.0001938407,0.000278394,0.5873405,0.05518049,0.03301301,0.004222174,0.3165974],"study_design_scores_gemma":[0.000003761231,0.000012673,0.0003102313,0.000009776612,0.000006802432,0.0000253601,0.00001404967,0.9788319,0.006238071,0.01329905,0.001238298,0.00001007703],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02300058,0.001757638,0.9712881,0.0005249962,0.00006495068,0.00002607946,0.0002591851,0.001694611,0.001383834],"genre_scores_gemma":[0.4737929,0.00242414,0.5125219,0.0007000557,0.0001675362,0.0001480072,0.001239163,0.0004150289,0.008591286],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008131259,"threshold_uncertainty_score":0.01616788,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009019967519240106,"score_gpt":0.2189056568784814,"score_spread":0.2098856893592413,"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."}}