# A Survey on Visual Mamba
## 4 Visual Mamba in Application Fields
### Medical Visual Mamba
#### 4.2.1 Two-Dimensional Medical Images
Additionally, it preserves spatial information by extracting hierarchical characteristics through the use of patch merging layers and visual state space blocks. TM-UNet [60] introduced improvements to the bottleneck layer. This architecture proposes triplet SSMs as the bottleneck layer, marking the first attempt to combine spatial and channel data using a pure SSM technique. Current Mamba-based models miss out on possible advantages, because they are mostly developed from scratch. As a new Mamba-based model tailored explicitly for medical image segmentation tasks, Swin-UMamba [61] leveraged the strengths of ImageNet-based pretraining. Previous discussions have primarily concentrated on supervised learning methods, but other supervisory approaches have also been explored. Semi-Mamba-UNet [52] combines a visual Mamba-based U-shape encoder-decoder with a traditional CNNs-based UNet in a semi-supervised learning framework. In order to enhance feature learning, especially with regard to unlabeled data, it presents a self-supervised pixel-by-pixel contrastive learning approach that makes use of two projectors. Weak-Mamba-UNet [76] refers to a novel weakly-supervised learning structure for medical image segmentation, combining CNNs, ViT, and the VMamba. It employs a cooperative, cross-supervisory approach using pseudo labels for iterative network learning and improvement, with a focus on scribble-based annotations. Some segmentation approaches diverge from UNet architectures. P-Mamba [117] presents a novel dual-branch framework for highly efficient left ventricle segmentation in pediatric echocardiograms. This model features an innovative DWT-based encoder branch equipped with Perona-Malik diffusion (PMD) blocks. Moreover, to bolster computational and memory efficiency, P-Mamba adopts vision Mamba layers within its vision mamba encoder branch. PromptMamba [78] represents a groundbreaking integration of vision Mamba and prompt technologies, marking a significant milestone as the first model to leverage the Mamba framework for the specific task of polyp segmentation. In addition, Mamba has also expanded into research in 2D medical imaging beyond segmentation, enhancing the precision and speed of image analysis to support diagnosis and treatment planning. Classification is a vital and fundamental task in the area of analysis of medical images. [PERSON] et al. invented Vision Mamba for this purpose, which was also known as MedMamba [65]. They created a brand-new module called Conv-SSM, which combines SSM's long-range dependency capture with a convolutional layers' local feature extraction. This enables efficient modeling of medical images from different modalities. Furthermore, MambaMIL [118] introduced the Sequence Reordering Mamba (SR-Mamba), a model recognizing the order and distribution of instances in long sequences, to effectively harness valuable embedded information. Since accurate and efficient clinical decision-making depends on high-quality and high-fidelity medical pictures, image reconstruction plays a critical role in improving diagnostic processes. [PERSON] et al. [56] developed MambaMIR, a model leveraging Mamba technology for the reconstruction of medical images, alongside its advanced counterpart, MambaMIR-GAN, which incorporates generative adversarial networks. [PERSON] et al. introduced FDVision Mamba (FDVM-Net) [81], a frequency-domain-based structure that effectively corrects image exposure by restoring endoscopic pictures' frequency domain, as recorded endoscopic images often suffer from exposure abnormalities. As shown in specialized areas, MD-Dose [90], a cutting-edge diffusion model based on the Mamba architecture, was designed to accurately predict radiation therapy dose distribution for thoracic cancer patients.