最近,北京大学的研究团队提出了一种全新的图像分割模型——LightM-UNet。这个模型将Mamba设计理念与UNet网络相结合,以极低的计算成本实现了卓越的分割性能。相较于传统的nnU-Net,LightM-UNet的参数仅为1.8M,计算量减少了21倍,而精度依然保持在顶级水平。这种结合策略充分利用了Mamba和UNet在图像处理和网络架构上的优...
具体来说,LightM-UNet利用纯Mamba方式的残差视觉Mamba层来提取深层语义特征和模拟长距离空间依赖性,计算复杂度为线性。在两个真实世界的2D/3D数据集上进行的广泛实验表明,LightM-UNet超越了现有的最先进文献。特别是与著名的nnU-Net相比,Li...
LightM-UNet:一种轻量级 Mamba UNet,它将 Mamba 和 UNet 集成在一个轻量级框架中,实现了卓越的分割性能,同时将参数和计算成本分别大幅降低了 116 倍和 21 倍!代码即将开源! 点击关注 @CVer官方知乎账号,可…
具体来说,LightM-UNet利用纯Mamba方式的残差视觉Mamba层来提取深层语义特征和模拟长距离空间依赖性,计算复杂度为线性。在两个真实世界的2D/3D数据集上进行的广泛实验表明,LightM-UNet超越了现有的最先进文献。特别是与著名的nnU-Net相比,LightM-UNet在大幅降低参数和计算成本116倍和21倍的同时,实现了更优越的分割性...
To cope with this issue, we developed Light-UNet, a segmentation model that reduces parameters and computational complexity while achieving superior performance, making it ideal for point-of-care and mobile devices medical imaging applications. Specifically, we proposed Small and Robust U-block (SRU)...
具体来说,LightM-UNet以纯Mamba的方式利用残余视觉曼巴层来提取深度语义特征,并建模长期空间依赖关系,具有线性计算复杂度。代码实现可以在https: //github.com/MrBlankness/上公开获得。 2 Introduction 作为一种基于卷积神经网络模型,UNet正在努力处理卷积操作的固有局部性,这限制了其理解显式全局和远程语义信息交互的...
3DUnet_lightsheet_boundary 3DUnet_lightsheet_nuclei root_nuclei_t30_pred.png root_nuclei_t30_raw.png test_config.yaml train_config.yaml 3DUnet_multiclass logo_small_80.png sample_ovule.h5 tests .bumpversion.cfg .gitignore LICENSE README.md environment.yaml setup.py Breadcrumbs pytorch-3d...
To this end, we present an efficient and lightweight model to identify cloud regions, referred to as Refined UNet lite, which is able to facilitate end-to-end training and inference and partially contributes to edge-precise cloud detection. Specifically, the UNet backbone locates cloud regions ...
(LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, with linear computational complexity. Extensive experiments ...
To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and UNet in a lightweight framework. Specifically, LightM-UNet leverages the Residual Vision Mamba Layer in a pure Mamba fashion to extract deep semantic features and model long-range spatial dependencies, ...