Cell　localization　still　faces　two　unresolved　challenges:　1)　the　dramatic　variations　in　cell　morphology,　coupled　with　the　heterogeneous　intensity　distribution　of　lightly　stained　cells;　2)　existing　cell　location　maps　lack　scale　information,　resulting　in　insufficient　supervision　for　point　maps　and　inaccurate　supervision　for　density　maps.　1)　To　address　the　first　challenges,　we　introduce　a　novel　gradient-aware　and　shape-adaptive　Difference-Deformable　Convolution　(DDConv),　which　enhances　the　model＇s　robustness　to　color　by　leveraging　gradient　information　while　adaptively　adjusting　the　shape　of　the　convolutional　kernel　to　tackle　the　substantial　variability　in　cell　morphology.　2)　To　overcome　the　issue　of　unreasonable　location　maps,　we　propose　the　Pseudo-Scale　Instance　(PSI)　map,　which　can　adaptively　provide　the　corresponding　scale　information　for　each　cell　to　realize　accurate　supervision.　We　analyze　and　evaluate　DDConv　and　the　PSI　map　in　three　challenging　cell　localization　tasks.　In　comparison　to　existing　methods,　our　proposed　approach　significantly　enhances　localization　performance,　setting　a　new　benchmark　for　the　cell　localization　task.