{"id":"W4302362316","doi":"10.32920/21287928","title":"Assessing the Impact of Color Normalization in Convolutional Neural Network-Based Nuclei Segmentation Frameworks","year":2022,"lang":"en","type":"preprint","venue":"","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. Michael's Hospital; Toronto Metropolitan University","funders":"Mitacs","keywords":"Convolutional neural network; Segmentation; Normalization (sociology); Preprocessor; Artificial intelligence; Computer science; Deep learning; Pattern recognition (psychology); Data pre-processing; Image segmentation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0075552,0.002360198,0.000800423,0.001526147,0.0007905418,0.001842506,0.002119811,0.001748076,0.001907945],"category_scores_gemma":[0.02000898,0.000496567,0.000800867,0.001128464,0.001261843,0.002994638,0.002086726,0.001695851,0.0006867805],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003136641,"about_ca_system_score_gemma":0.002550781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03146167,"about_ca_topic_score_gemma":0.02564452,"domain_scores_codex":[0.997146,0.0007966598,0.0001418149,0.0006020401,0.0008572418,0.0004562535],"domain_scores_gemma":[0.992631,0.003307304,0.0007147657,0.001162481,0.001821577,0.0003629127],"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":[0.001648797,0.0005848894,0.01472859,0.0006173137,0.0004997369,0.0001271417,0.0001323547,0.694098,0.01674537,0.008876877,0.0056639,0.2562771],"study_design_scores_gemma":[0.00003193252,0.00031578,0.002933699,0.00006935483,0.00009365363,0.00005559595,0.00005492617,0.9715137,0.02092019,0.002427831,0.001556155,0.00002716378],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6764561,0.01020123,0.2781405,0.002250393,0.0006920577,0.0004506648,0.001456884,0.009796127,0.02055609],"genre_scores_gemma":[0.8930144,0.001599128,0.09930319,0.0005588305,0.0001140989,0.0001038716,0.001426426,0.0008299952,0.003050148],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03146167,"threshold_uncertainty_score":0.0625571,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0289481662865543,"score_gpt":0.3453888592991942,"score_spread":0.3164406930126399,"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."}}