TransRUPNet for Improved Polyp Segmentation
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Abstract (arXiv)
Colorectal cancer is among the most common cause of cancer worldwide. Removal of precancerous polyps through early detection is essential to prevent them from progressing to colon cancer. We develop an advanced deep learning-based architecture, Transformer based Residual Upsampling Network (TransRUPNet) for automatic and real-time polyp segmentation. The proposed architecture, TransRUPNet, is an encoder-decoder network consisting of three encoder and decoder blocks with additional upsampling blocks at the end of the network. With the image size of 256×256, the proposed method achieves an excellent real-time operation speed of 47.07 frames per second with an average mean dice coefficient score of 0.7786 and mean Intersection over Union of 0.7210 on the out-of-distribution polyp datasets. The results on the publicly available PolypGen dataset suggest that TransRUPNet can give real-time feedback while retaining high accuracy for in-distribution datasets. Furthermore, we demonstrate the generalizability of the proposed method by showing that it significantly improves performance on out-of-distribution datasets compared to the existing methods. The source code of our network is available at https://github.com/DebeshJha/TransRUPNet.
Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 15 of its 23 references that are in the map. First posted on arXiv 3 Jun 2023.
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References · 15
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- 2021NeoUNet: Towards accurate colon polyp segmentation and neoplasm detectioncited by 50
3 more
- 2022ColonFormer: An Efficient Transformer based Method for Colon Polyp Segmentationcited by 28
- 2022Assessing generalisability of deep learning-based polyp detection and segmentation methods through a computer vision challengecited by 27
- 2023TransNetR: Transformer-based Residual Network for Polyp Segmentation with Multi-Center Out-of-Distribution Testingcited by 26
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