{"id":"W4390484879","doi":"10.7150/thno.90246","title":"Deep learning for automatic organ and tumor segmentation in nanomedicine pharmacokinetics","year":2024,"lang":"en","type":"article","venue":"Theranostics","topic":"Radiomics and Machine Learning in Medical Imaging","field":"Medicine","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Vector Institute; Princess Margaret Cancer Centre; University of Toronto; Canada Research Chairs; University Health Network","funders":"Canadian Institutes of Health Research; Canada Research Chairs; Prostate Cancer Canada; Princess Margaret Cancer Foundation","keywords":"Segmentation; Computer science; Artificial intelligence; Deep learning; Nanomedicine; Medical imaging; Pharmacokinetics; Dosimetry; Medical physics; Pattern recognition (psychology); Medicine; Nuclear medicine; Pharmacology; Materials science; Nanoparticle; Nanotechnology","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":[0.0003236613,0.0001096044,0.0001980627,0.0001417035,0.00002974448,0.00003338703,0.00003456599,0.00003125346,0.00003572381],"category_scores_gemma":[0.0004040181,0.00008940778,0.00002611016,0.0001676947,0.00006566072,0.00003457717,0.00001408887,0.0003052055,0.000002724815],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004754245,"about_ca_system_score_gemma":0.00003820644,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000937784,"about_ca_topic_score_gemma":0.000001151116,"domain_scores_codex":[0.9992552,0.0000313724,0.0002209543,0.0001707584,0.0001395842,0.0001821382],"domain_scores_gemma":[0.9994226,0.0003685224,0.0000293232,0.00006303631,0.00002749211,0.00008900674],"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":[0.0001354524,0.000128038,0.02265222,0.002751046,0.0001732642,0.0003747634,0.007623123,0.001726792,0.2053215,0.0008972879,0.0006956225,0.7575209],"study_design_scores_gemma":[0.00197928,0.0002585362,0.004584693,0.0005089727,0.000163819,0.0001258472,0.0002923445,0.9841452,0.001164521,0.0002999685,0.006370543,0.0001062567],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9119285,0.004751782,0.07793489,0.003713486,0.0004536716,0.0005800549,9.653112e-7,0.0004782786,0.0001583254],"genre_scores_gemma":[0.9876238,0.0004653217,0.01032868,0.0009524543,0.0002749635,0.00001983499,0.00002419615,0.00008647914,0.0002243207],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9824184,"threshold_uncertainty_score":0.3645943,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009163927886860414,"score_gpt":0.3097853133810424,"score_spread":0.300621385494182,"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."}}