Colorectal　cancer　(CRC)　is　the　second　most　common　cause　of　cancer　mortality　worldwide.　Colonoscopy　is　a　widely　used　technique　for　colon　screening　and　polyp　lesions　diagnosis.　Nevertheless,　manual　screening　using　colonoscopy　suffers　from　a　substantial　miss　rate　of　polyps　and　is　an　overwhelming　burden　for　endoscopists.　Computer-aided　diagnosis　(CAD)　for　polyp　detection　has　the　potential　to　reduce　human　error　and　human　burden.　However,　current　polyp　detection　methods　based　on　object　detection　framework　need　many　handcrafted　preprocessing　and　post-processing　operations　or　user　guidance　that　require　domain-specific　knowledge.　In　this　paper,　we　propose　a　convolution　in　transformer　(COTR)　network　for　end-to-end　polyp　detection.　Motivated　by　the　detection　transformer　(DETR),　COTR　is　constituted　by　a　CNN　for　feature　extraction,　transformer　encoder　layers　interleaved　with　convolutional　layers　for　feature　encoding　and　recalibration,　transformer　decoder　layers　for　object　querying,　and　a　feed-forward　network　for　detection　prediction.　Considering　the　slow　convergence　of　DETR,　COTR　embeds　convolution　layers　into　transformer　encoder　for　feature　reconstruction　and　convergence　acceleration.　Experimental　results　on　two　public　polyp　datasets　show　that　COTR　achieved　91.49%　precision,　82.69%　sensitivity,　and　86.87%　F1-score　on　the　ETIS-LARIB,　and　91.67%　precision,　93.54%　sensitivity,　and　92.60%　F1-score　on　the　CVC-ColonDB.　©　2021　IEEE.