Polyps are early cancer indicators, so assessing occurrences of polyps and their removal is critical. They are observed through a colonoscopy screening procedure that generates a stream of video frames. Segmenting polyps in their natural video screening procedure has several challenges, such as the co-existence of imaging artefacts, motion blur, and floating debris. Most existing polyp segmentation algorithms are developed on curated still image datasets that do not represent real-world colonoscopy. Their performance often degrades on video data. We propose a video polyp segmentation method that performs self-supervised learning as an auxiliary task and a spatial-temporal self-attention mechanism for improved representation learning. Our end-to-end configuration and joint optimisation of losses enable the network to learn more discriminative contextual features in videos. Our experimental results demonstrate an improvement with respect to several state-of-the-art (SOTA) methods. Our ablation study also confirms that the choice of the proposed joint end-to-end training improves network accuracy by over 3% and nearly 10% on both the Dice similarity coefficient and intersection-over-union compared to the recently proposed method PNS+ and Polyp-PVT, respectively. Results on previously unseen video data indicate that the proposed method generalises.

通过自监督学习作为辅助任务以及空间-时间自注意机制，我们提出了一种视频息肉分割方法，以改进表示学习。我们的端到端配置和联合优化损失使网络能够在视频中学习更有区分性的上下文特征。实验结果表明，与多个最先进方法相比，我们的方法有所改进。我们的消融研究还证实了所提出的端到端训练选择相比最近提出的方法 PNS+ 和 Polyp-PVT，在 Dice 相似系数和交并比方面，网络准确性提高了3%以上，并分别与之相比提高了近10%。对之前未见的视频数据的结果表明，所提出的方法具有泛化能力。

SSTFB: 利用自监督预训练和时间自注意力以特征分支实现实时视频息肉分割