Universal　lesion　detection　using　computerised　tomography　(CT)　scans　is　a　critical　computer-aided　diagnosis　measure　in　clinical　diagnosis.　One　of　the　key　issues　during　the　diagnosis　is　to　identify　the　correlations　between　sequential　slices　to　improve　the　feature　representation　of　CT　scans.　In　the　process　of　fusing　slice　features　containing　temporal　correlations,　the　correlation　between　the　contextual　slices　in　the　channel　dimension　and　the　target　slices　is　closely　related　to　the　spatial　distance　in　practice.　However,　convolutional　fusion　approaches　commonly　ignore　that　features　of　different　distances　have　unequal　weights.　To　tackle　this　issue,　we　present　a　temporal　correlation　weighted　fusion　lesion　detection　network,　called　TCW-Net.　Specifically,　for　the　slices　in　the　channel　dimension,　we　develop　a　weighted　feature　fusion　module　to　adjust　the　more　discriminative　features　using　learned　weights.　Then,　we　adapt　a　spatial　offset　attention　mechanism　that　allows　the　detection　network　to　pay　more　attention　to　the　lesion＇s　slight　spatial　offset　and　thus　improve　the　model＇s　capacity　for　distinguishing　between　different　lesion　features.　Extensive　experiments　carried　out　on　the　DeepLesion　dataset　show　that　the　proposed　algorithm　has　superior　performance　over　the　state-of-the-art　methods.