COTR: Convolution in Transformer Network for End to End Polyp Detection
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. Computeraided 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. 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% F1score 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 ofthe art methods.
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