Cell　localization　constitutes　a　fundamental　research　domain　within　the　realm　of　pathology　image　analysis,　with　its　core　objective　being　the　precise　identification　of　cell　spatial　coordinates.　The　task　has　always　involved　the　challenge　of　large　color　variations　among　cells,　uneven　distribution,　and　overlapping　borders.　Furthermore,　in　realistic　cell　localization　scenarios,　the　existing　state-of-the-art　methods　suffer　from　high　computational　costs　and　slow　inference　times,　which　severely　reduce　the　efficiency　of　computer-assisted.　To　tackle　the　above　issues,　a　lightweight　and　efficient　cell　localization　model　named　Lite-UNet　is　proposed.　Specifically,　the　Lite-UNet　encompasses　three　pivotal　modules.　Firstly,　we　introduce　a　gradient　aggregation　module　grounded　in　difference　convolution.　This　module　effectively　mitigates　the　challenge　posed　by　extensive　color　variations　among　cells　by　adeptly　leveraging　gradient　information.　Secondly,　we　propose　an　efficient　plug-and-play　graph　correlation　attention　module,　which　optimizes　the　feature　representation　capabilities　by　encoding　higher-order　feature　associations.　Finally,　we　design　a　lightweight　Ghost_CBAM　module　that　alleviates　the　difficulty　of　uneven　cell　distribution　while　forming　the　base　module　of　the　Lite-UNet.　Extensive　experiments　show　that　our　LiteUNet　is　capable　of　locating　cells　in　images　quickly　and　accurately,　thus　further　improving　the　efficiency　of　computer-assisted　medicine.