Digital　medical　imaging,　particularly　pathology　images,　is　essential　for　cancer　diagnosis　but　faces　challenges　in　direct　model　training　due　to　its　super-resolution　nature.　Although　weakly　supervised　learning　has　reduced　the　need　for　manual　annotations,　many　multiple　instance　learning　(MIL)　methods　struggle　to　effectively　capture　crucial　spatial　relationships　in　histopathological　images.　Existing　methods　incorporating　positional　information　often　overlook　nuanced　spatial　correlations　or　use　positional　encoding　strategies　that　do　not　fully　capture　the　unique　spatial　dynamics　of　pathology　images.　To　address　this　issue,　we　propose　a　new　framework　named　TMIL　(Transformer-based　Multiple　Instance　Learning　Network　with　2D　positional　encoding),　which　leverages　multiple　instance　learning　for　weakly　supervised　classification　of　histopathological　images.　TMIL　incorporates　a　2D　positional　encoding　module,　based　on　the　Transformer,　to　model　positional　information　and　explore　correlations　between　instances.　Furthermore,　TMIL　divides　histopathological　images　into　pseudo-bags　and　trains　patch-level　feature　vectors　with　deep　metric　learning　to　enhance　classification　performance.　Finally,　the　proposed　approach　is　evaluated　on　a　public　colorectal　adenoma　dataset.　The　experimental　results　show　that　TMIL　outperforms　existing　MIL　methods,　achieving　an　AUC　of　97.28%　and　an　ACC　of　95.19%.　These　findings　suggest　that　TMIL’s　integration　of　deep　metric　learning　and　positional　encoding　offers　a　promising　approach　for　improving　the　efficiency　and　accuracy　of　pathology　image　analysis　in　cancer　diagnosis.　©　The　Author(s)　2025.