Background:　Automated　tumor　segmentation　and　survival　prediction　are　critical　to　clinical　diagnosis　and　treatment.　This　study　aimed　to　develop　deep-learning　models　for　automatic　tumor　segmentation　and　survival　prediction　in　magnetic　resonance　imaging　(MRI)　of　cervical　cancer　(CC)　by　combining　deep　neural　networks　and　Transformer　architecture.　Methods:　This　study　included　406　patients　with　CC,　each　with　comprehensive　clinical　information　and　MRI　scans.　We　randomly　divided　patients　into　training,　validation,　and　independent　test　cohorts　in　a　6:2:2　ratio.　During　the　model　training,　we　employed　two　architecture　types:　one　being　a　hybrid　model　combining　convolutional　neural　network　(CNN)　and　ransformer　(CoTr)　and　one　of　pure　CNNs.　For　survival　prediction,　the　hybrid　model　combined　tumor　image　features　extracted　by　segmentation　models　with　clinical　information.　The　performance　of　the　segmentation　models　was　evaluated　using　the　Dice　similarity　coefficient　(DSC)　and　95%　Hausdorff　distance　(HD95).　The　performance　of　the　survival　models　was　assessed　using　the　concordance　index　.　Results:　The　CoTr　model　performed　well　in　both　contrast-enhanced　T1-weighted　(ceT1W)　and　T2-weighted　(T2W)　imaging　segmentation　tasks,　with　average　DSCs　of　0.827　and　0.820,　respectively,　which　outperformed　other　the　CNN　models　such　as　U-Net　(DSC:　0.807　and　0.808),　attention　U-Net　(DSC:　0.814　and　0.811),　and　V-Net　(DSC:　0.805　and　0.807).　For　survival　prediction,　the　proposed　deep-learning　model　significantly　outperformed　traditional　methods,　yielding　a　concordance　index　of　0.732.　Moreover,　it　effectively　divided　patients　into　low-risk　and　high-risk　groups　for　disease　progression　(P＜0.001).　Conclusions:　Combining　Transformer　architecture　with　a　CNN　can　improve　MRI　tumor　segmentation,　and　this　deep-learning　model　excelled　in　the　survival　prediction　of　patients　with　CC　as　compared　to　traditional　methods.