# COTR: Convolution in Transformer Network for End to End Polyp Detection

arXiv: https://arxiv.org/abs/2105.10925 · HTML: https://mlsanity.com/paper/2105.10925/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 24 references that are in the map.
- First posted on arXiv 23 May 2021.

## Builds on (most cited first, 6 of 6)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression](https://mlsanity.com/paper/1902.09630/) (2019)
- [Automatic Colon Polyp Detection using Region based Deep CNN and Post Learning Approaches](https://mlsanity.com/paper/1906.11463/) (2019)

## Built on by (most cited first, 19 of 19)

- [Transformers in Medical Imaging: A Survey](https://mlsanity.com/paper/2201.09873/) (2022)
- [Advances in Medical Image Analysis with Vision Transformers: A Comprehensive Review](https://mlsanity.com/paper/2301.03505/) (2023)
- [Transformers in Medical Image Analysis: A Review](https://mlsanity.com/paper/2202.12165/) (2022)
- [Transforming medical imaging with Transformers? A comparative review of key properties, current progresses, and future perspectives](https://mlsanity.com/paper/2206.01136/) (2022)
- [A survey on attention mechanisms for medical applications: are we moving towards better algorithms?](https://mlsanity.com/paper/2204.12406/) (2022)
- [Focused Decoding Enables 3D Anatomical Detection by Transformers](https://mlsanity.com/paper/2207.10774/) (2022)
- [Revisiting Computer-Aided Tuberculosis Diagnosis](https://mlsanity.com/paper/2307.02848/) (2023)
- [TRUSTED: The Paired 3D Transabdominal Ultrasound and CT Human Data for Kidney Segmentation and Registration Research](https://mlsanity.com/paper/2310.12646/) (2023)
- [D-TrAttUnet: Dual-Decoder Transformer-Based Attention Unet Architecture for Binary and Multi-classes Covid-19 Infection Segmentation](https://mlsanity.com/paper/2303.15576/) (2023)
- [D-TrAttUnet: Toward Hybrid CNN-Transformer Architecture for Generic and Subtle Segmentation in Medical Images](https://mlsanity.com/paper/2405.04169/) (2024)
- [Understanding differences in applying DETR to natural and medical images](https://mlsanity.com/paper/2405.17677/) (2024)
- [An unsupervised approach towards promptable defect segmentation in laser-based additive manufacturing by Segment Anything](https://mlsanity.com/paper/2312.04063/) (2023)
- [Detection Transformer for Teeth Detection, Segmentation, and Numbering in Oral Rare Diseases: Focus on Data Augmentation and Inpainting Techniques](https://mlsanity.com/paper/2402.04408/) (2024)
- [Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces](https://mlsanity.com/paper/2204.03408/) (2022)
- [Polyp segmentation in colonoscopy images using DeepLabV3++](https://mlsanity.com/paper/2407.19327/) (2024)
- [MTMed3D: A Multi-Task Transformer-Based Model for 3D Medical Imaging](https://mlsanity.com/paper/2511.12373/) (2025)
- [GRAFNet: Multiscale Retinal Processing via Guided Cortical Attention Feedback for Enhancing Medical Image Polyp Segmentation](https://mlsanity.com/paper/2602.15072/) (2026)
- [BifDet: A 3D Bifurcation Detection Dataset for Airway-Tree Modeling](https://mlsanity.com/paper/2604.24999/) (2026)
- [Prediction of Rectal Cancer Regrowth from Longitudinal Endoscopy](https://mlsanity.com/paper/2605.12855/) (2026)

## Abstract (arXiv)

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.