Nuclei　segmentation　and　classification　play　a　crucial　role　in　pathology　diagnosis,　enabling　pathologists　to　analyze　cellular　characteristics　accurately.　Overlapping　cluster　nuclei,　misdetection　of　small-scale　nuclei,　and　pleomorphic　nuclei-induced　misclassification　have　always　been　major　challenges　in　the　nuclei　segmentation　and　classification　tasks.　To　this　end,　we　introduce　an　auxiliary　task　of　nuclei　boundary-guided　contrastive　learning　to　enhance　the　representativeness　and　discriminative　power　of　visual　features,　particularly　for　addressing　the　challenge　posed　by　the　unclear　contours　of　adherent　nuclei　and　small　nuclei.　In　addition,　misclassifications　resulting　from　pleomorphic　nuclei　often　exhibit　low　classification　confidence,　indicating　a　high　level　of　uncertainty.　To　mitigate　misclassification,　we　capitalize　on　the　characteristic　clustering　of　similar　cells　to　propose　a　locality-aware　class　embedding　module,　offering　a　regional　perspective　to　capture　category　information.　Moreover,　we　address　uncertain　classification　in　densely　aggregated　nuclei　by　designing　a　top-k　uncertainty　attention　module　that　leverages　deep　features　to　enhance　shallow　features,　thereby　improving　the　learning　of　contextual　semantic　information.　We　demonstrate　that　the　proposed　network　outperforms　the　off-the-shelf　methods　in　both　nuclei　segmentation　and　classification　experiments,　achieving　the　state-of-the-art　performance.　©　2024　Elsevier　Ltd