Vivim：一种用于医学视频分割的视频视觉Mamba
Vivim: a Video Vision Mamba for Medical Video Segmentation
AI总结：
本文提出Vivim，一种基于视频视觉Mamba的医学视频分割框架，通过时间Mamba块和改进的边界感知仿射约束有效建模长时空序列并增强模糊病灶判别能力，在甲状腺、乳腺和息肉分割任务上优于现有方法。
摘要：
医学视频分割由于视频帧中冗余的动态参考而在临床实践中受到越来越多的关注。然而，传统卷积神经网络的感受野有限，而基于Transformer的网络在计算复杂度的角度上构建长期依赖关系方面表现平庸。当使用内存有限的可用设备处理医学视频分析任务中更长的序列时，这一瓶颈构成了重大挑战。近年来，以Mamba闻名的状态空间模型（SSMs）在高效长序列建模方面展现出令人瞩目的成就，通过在诸多视觉任务上显著扩大感受野来发展深度神经网络。不幸的是，原始SSMs未能同时捕获因果时间线索并保留非因果空间信息。为此，本文提出了一种基于视频视觉Mamba的框架，称为Vivim，用于医学视频分割任务。我们的Vivim通过我们设计的时间Mamba块，能够有效地将长期时空表示压缩为不同尺度的序列。我们还引入了一种改进的跨帧边界感知仿射约束，以增强Vivim对模糊病灶的判别能力。在甲状腺分割、超声视频中的乳腺病灶分割以及结肠镜视频中的息肉分割上的大量实验证明了我们的Vivim的有效性和高效性，优于现有方法。代码可在https://github.com/scott-yjyang/Vivim获取。数据集将在被接收后发布。
英文摘要：
Medical video segmentation gains increasing attention in clinical practice due to the redundant dynamic references in video frames. However, traditional convolutional neural networks have a limited receptive field and transformer-based networks are mediocre in constructing long-term dependency from the perspective of computational complexity. This bottleneck poses a significant challenge when processing longer sequences in medical video analysis tasks using available devices with limited memory. Recently, state space models (SSMs), famous by Mamba, have exhibited impressive achievements in efficient long sequence modeling, which develops deep neural networks by expanding the receptive field on many vision tasks significantly. Unfortunately, vanilla SSMs failed to simultaneously capture causal temporal cues and preserve non-casual spatial information. To this end, this paper presents a Video Vision Mamba-based framework, dubbed as Vivim, for medical video segmentation tasks. Our Vivim can effectively compress the long-term spatiotemporal representation into sequences at varying scales with our designed Temporal Mamba Block. We also introduce an improved boundary-aware affine constraint across frames to enhance the discriminative ability of Vivim on ambiguous lesions. Extensive experiments on thyroid segmentation, breast lesion segmentation in ultrasound videos, and polyp segmentation in colonoscopy videos demonstrate the effectiveness and efficiency of our Vivim, superior to existing methods. The code is available at: https://github.com/scott-yjyang/Vivim. The dataset will be released once accepted.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
