Abdominal　tumor　segmentation　is　a　crucial　yet　challenging　step　during　the　screening　and　diagnosis　of　tumors.　While　3D　segmentation　models　provide　powerful　performance,　they　demand　substantial　computational　resources.　Additionally,　in　3D　data,　tumors　often　represent　a　small　portion,　leading　to　imbalanced　data　and　potentially　overlooking　crucial　information.　Conversely,　2D　segmentation　models　have　a　lightweight　structure,　but　disregard　the　inter-slice　correlation,　risking　the　loss　of　tumor　in　edge　slices.　To　address　these　challenges,　this　paper　proposes　a　novel　Position-Aware　and　Key　Slice　Feature　Sharing　2D　tumor　segmentation　model　(PAKS-Net).　Leveraging　the　Swin-Transformer,　we　effectively　model　the　global　features　within　each　slice,　facilitating　essential　information　extraction.　Furthermore,　we　introduce　a　Position-Aware　module　to　capture　the　spatial　relationship　between　tumors　and　their　corresponding　organs,　mitigating　noise　and　interference　from　surrounding　organ　tissues.　To　enhance　the　edge　slice　segmentation　accuracy,　we　employ　key　slices　to　assist　in　the　segmentation　of　other　slices　to　prioritize　tumor　regions.　Through　extensive　experiments　on　three　abdominal　tumor　segmentation　CT　datasets　and　a　lung　tumor　segmentation　CT　dataset,　PAKS-Net　demonstrates　superior　performance,　reaching　0.893,　0.769,　0.598　and　0.738　tumor　DSC　on　the　KiTS19,　LiTS17,　pancreas　and　LOTUS　datasets,　surpassing　3D　segmentation　models,　while　remaining　computationally　efficient　with　fewer　parameters.　©　2024