{"id":"W4417102935","doi":"10.1038/s41746-025-02196-8","title":"Self-supervised stain normalization empowers privacy-preserving and model generalization in digital pathology","year":2025,"lang":"en","type":"article","venue":"npj Digital Medicine","topic":"AI in cancer detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"Zhejiang University; Hangzhou Science and Technology Bureau; Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Normalization (sociology); Stain; Digital pathology; Database normalization; Pattern recognition (psychology); Generalization","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":[0.003538121,0.000615356,0.0008654893,0.0004841164,0.0004756393,0.00153684,0.002067225,0.001005551,0.0009706746],"category_scores_gemma":[0.007803419,0.000564752,0.0009832029,0.0006166976,0.00139507,0.002714936,0.002388108,0.00182542,0.0005372828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001198011,"about_ca_system_score_gemma":0.001565334,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003590761,"about_ca_topic_score_gemma":0.004177012,"domain_scores_codex":[0.9987364,0.0003131717,0.00005789654,0.00050107,0.0002911039,0.0001003705],"domain_scores_gemma":[0.9963397,0.001249885,0.0003388897,0.001586943,0.0003894641,0.00009519821],"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":[0.0004308199,0.0002327557,0.008632128,0.0000940261,0.0001584117,0.0002597718,0.0004383913,0.7088035,0.01740854,0.01222399,0.005580341,0.2457372],"study_design_scores_gemma":[0.000008764783,0.000030556,0.0004820496,0.000004777316,0.00001029281,0.00005734176,0.00002239488,0.9858842,0.005306677,0.007352289,0.0008325898,0.000008021385],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07389839,0.0001973473,0.920184,0.0005283554,0.00004106449,0.00007200295,0.0002873018,0.003526219,0.001265352],"genre_scores_gemma":[0.7988381,0.0002078442,0.1949735,0.0004778223,0.00007772433,0.000141263,0.001147238,0.0003750579,0.003761433],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003590761,"threshold_uncertainty_score":0.01871157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009580765354644742,"score_gpt":0.2523878049224137,"score_spread":0.242807039567769,"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."}}