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 $256\times256$, 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.

翻译：结直肠癌是全球最常见的癌症之一。通过早期检测切除癌前息肉，对于防止其发展为结肠癌至关重要。我们提出了一种基于深度学习的先进架构——基于Transformer的残差上采样网络（TransRUPNet），用于自动且实时的息肉分割。该架构TransRUPNet是一种编码器-解码器网络，包含三个编码器和解码器模块，并在网络末端增加了额外的上采样模块。在图像尺寸为$256\times256$时，所提方法在分布外息肉数据集上实现了47.07帧/秒的优异实时运行速度，平均Dice系数为0.7786，平均交并比为0.7210。在公开可用的PolypGen数据集上的结果表明，TransRUPNet能够在保持高精度的同时提供实时反馈，适用于分布内数据集。此外，我们通过实验证明了所提方法的泛化能力：与现有方法相比，它在分布外数据集上的性能显著提升。本网络的源代码可在https://github.com/DebeshJha/TransRUPNet获取。