Ovarian　cancer　presents　a　notable　health　concern　characterized　by　its　unfavorable　prognosis　and　elevated　mortality　rates　in　the　female　population.　Accurate　prognostic　assessment　is　essential　for　tailoring　treatment　strategies　and　improving　patient　outcomes.　Analysis　of　histopathological　whole-slide　images　is　the　gold　standard　for　pathological　diagnosis,　which　contains　rich　phenotypic　and　molecular　information.　Multiple　instance　learning　methods　have　been　the　dominant　approach　for　processing　megapixel　whole　slide　images.　However,　the　methods　adopt　the　one　image　as　a　bag　strategy,　which　will　contain　many　noisy　tiles　leading　to　model　overfitting　during　training.　To　mitigate　the　above　situation,　we　propose　a　transformer-based　multi-instance　learning　framework　with　a　pseudo-bag　strategy　(TransPBMIL)　for　predicting　overall　survival　within　3　years　of　ovarian　cancer　patients　using　pathological　images.　Extensive　studies　on　multiple　cancer　prognostic　datasets　demonstrate　the　superiority　of　TransPBMIL.