{"id":"W4411799550","doi":"10.1109/tfuzz.2025.3583819","title":"FCAformer: Fuzzy-Enhanced Class-Aware Attention Based Transformer for Weakly Supervised Histopathology Image Segmentation","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Fuzzy Systems","topic":"AI in cancer detection","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"Natural Science Foundation of Jiangsu Province; National Natural Science Foundation of China; Natural Science Foundation of Nantong City","keywords":"Image segmentation; Artificial intelligence; Computer science; Segmentation; Pattern recognition (psychology); Computer vision; Fuzzy logic; Class (philosophy); Scale-space segmentation; Transformer; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004874903,0.0003601615,0.000418586,0.0006462384,0.0005964086,0.0002131686,0.0005349579,0.0003571167,0.00001482601],"category_scores_gemma":[0.000005403121,0.0003800135,0.0003454533,0.0008642999,0.00008681003,0.0009809464,0.000001610307,0.000392575,0.00007233802],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001108748,"about_ca_system_score_gemma":0.00036874,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001190999,"about_ca_topic_score_gemma":0.0001381023,"domain_scores_codex":[0.9973,0.0002223257,0.0007219092,0.0008407252,0.0004037118,0.0005112936],"domain_scores_gemma":[0.9984264,0.0002148088,0.0001828529,0.0007180706,0.0003506628,0.0001072326],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0006544527,0.0006014363,0.0000237198,0.001383388,0.0001754171,0.000008116429,0.001532593,0.025323,0.8138294,0.001496243,0.002155553,0.1528167],"study_design_scores_gemma":[0.007704942,0.001620512,0.0004339414,0.000657152,0.0003318236,0.00004146859,0.001057225,0.4529094,0.5275172,0.00115281,0.005279077,0.00129448],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006237085,0.00007578223,0.9797599,0.001220453,0.007914634,0.001991572,0.0000958382,0.0005671869,0.002137513],"genre_scores_gemma":[0.9884907,0.00002322656,0.006901863,0.0003731957,0.0000937937,0.001960988,0.00002451916,0.0000384297,0.002093269],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9822536,"threshold_uncertainty_score":0.9998652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01425772190657761,"score_gpt":0.2641116513675389,"score_spread":0.2498539294609613,"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."}}