Cell　localization　in　medical　image　analysis　is　a　challenging　task　due　to　the　significant　variation　in　cell　shape,　size　and　color.　Existing　localization　methods　continue　to　tackle　these　challenges　separately,　frequently　facing　complications　where　these　difficulties　intersect　and　adversely　impact　model　performance.　In　this　paper,　these　challenges　are　first　reframed　as　issues　of　feature　misalignment　between　cell　images　and　location　maps,　which　are　then　collectively　addressed.　Specifically,　we　propose　a　feature　alignment　model　based　on　a　multiscale　hypergraph　attention　network.　The　model　considers　local　regions　in　the　feature　map　as　nodes　and　utilizes　a　learnable　similarity　metric　to　construct　hypergraphs　at　various　scales.　We　then　utilize　a　hypergraph　convolutional　network　to　aggregate　the　features　associated　with　the　nodes　and　achieve　feature　alignment　between　the　cell　images　and　location　maps.　Furthermore,　we　introduce　a　stepwise　adaptive　fusion　module　to　fuse　features　at　different　levels　effectively　and　adaptively.　The　comprehensive　experimental　results　demonstrate　the　effectiveness　of　our　proposed　multi　-scale　hypergraph　attention　module　in　addressing　the　issue　of　feature　misalignment,　and　our　model　achieves　state-of-the-art　performance　across　various　cell　localization　datasets.