  Purpose: 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
pre-processing and post-processing operations or user guidance that require
domain-specific knowledge.
  Methods: 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.
  Results: 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.
  Conclusion: This study proposed an end to end detection method based on
detection transformer for colorectal polyp detection. Experimental results on
ETIS-LARIB and CVC-ColonDB dataset demonstrated that the proposed model
achieved comparable performance against state-of-the-art methods.
