用扩散模型提升视频肠镜息肉分割，解决伪装与时序冗余难题。
Diff-VPS: Video Polyp Segmentation via a Multi-task Diffusion Network with Adversarial Temporal Reasoning
- 多任务监督增强扩散模型像素级分割能力
- 时序推理模块生成更真实帧，捕捉动态特征
- 适合医学图像分割与扩散模型应用研究者
扩散概率模型因其卓越性能在计算机视觉领域受到广泛关注。尽管大量基于扩散的研究聚焦于生成任务，但尚未有工作将其应用于视频息肉分割，该任务常受息肉高伪装性与冗余时序信息的挑战。本文提出一种新型扩散网络Diff-VPS，用于视频息肉分割。通过将多任务监督引入扩散模型，提升其像素级分割的判别能力，并融合分类与检测任务获得的上下文高层信息。为探索时序依赖关系，设计了时序推理模块（TRM），通过从前帧推理并重建目标帧实现时序建模。进一步采用生成对抗自监督策略增强TRM，生成更逼真的帧以捕捉更好的动态线索。在SUN-SEG数据集上的大量实验表明，所提Diff-VPS显著达到当前最优性能。代码已开源。
原文摘要 · Abstract (English)
Diffusion Probabilistic Models have recently attracted significant attention in the community of computer vision due to their outstanding performance. However, while a substantial amount of diffusion-based research has focused on generative tasks, no work introduces diffusion models to advance the results of polyp segmentation in videos, which is frequently challenged by polyps' high camouflage and redundant temporal cues.In this paper, we present a novel diffusion-based network for video polyp segmentation task, dubbed as Diff-VPS. We incorporate multi-task supervision into diffusion models to promote the discrimination of diffusion models on pixel-by-pixel segmentation. This integrates the contextual high-level information achieved by the joint classification and detection tasks. To explore the temporal dependency, Temporal Reasoning Module (TRM) is devised via reasoning and reconstructing the target frame from the previous frames. We further equip TRM with a generative adversarial self-supervised strategy to produce more realistic frames and thus capture better dynamic cues. Extensive experiments are conducted on SUN-SEG, and the results indicate that our proposed Diff-VPS significantly achieves state-of-the-art performance. Code is available at https://github.com/lydia-yllu/Diff-VPS.
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