{"id":"W4417214487","doi":"10.1016/j.knosys.2025.115096","title":"Mix-domain contrastive learning with Mamba generator for unpaired H&E-to-IHC stain translation","year":2025,"lang":"en","type":"article","venue":"Knowledge-Based Systems","topic":"AI in cancer detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"China Postdoctoral Science Foundation; Beijing University of Posts and Telecommunications; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Purdue University","keywords":"Generator (circuit theory); Translation (biology); Perspective (graphical); Stain; Pattern recognition (psychology); Contrastive analysis; Matching (statistics)","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":[0.0007600199,0.0008239457,0.000524171,0.0006477559,0.0004140573,0.000693663,0.001454488,0.001203685,0.0133185],"category_scores_gemma":[0.001685257,0.0003244965,0.0006590478,0.0005692078,0.0003893145,0.0008937998,0.001449243,0.001327414,0.005435407],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000437477,"about_ca_system_score_gemma":0.0007948031,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001913894,"about_ca_topic_score_gemma":0.003225618,"domain_scores_codex":[0.9997053,0.00005742104,0.0000129149,0.000106484,0.00006924484,0.00004872377],"domain_scores_gemma":[0.999488,0.0001864623,0.00002370058,0.00009896991,0.0001725924,0.00003019034],"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":[0.0005338795,0.0002347956,0.0009182849,0.0002607766,0.00007372074,0.0001876833,0.00009990857,0.04108937,0.0626388,0.009938761,0.01628951,0.8677345],"study_design_scores_gemma":[0.00004117891,0.0001432358,0.0006712472,0.00002579477,0.0000357081,0.0001991513,0.00005379103,0.8958648,0.07696555,0.01377226,0.0121989,0.00002837753],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0112732,0.0002826209,0.9760379,0.0001968952,0.0001530599,0.0001182351,0.0004427274,0.007905622,0.003589717],"genre_scores_gemma":[0.2445015,0.0002290282,0.7389398,0.0004610052,0.00009157037,0.0003956053,0.001906876,0.0005856847,0.01288893],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0133185,"threshold_uncertainty_score":0.04455483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02260411086727553,"score_gpt":0.2733293106141554,"score_spread":0.2507251997468799,"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."}}