为此,我们介绍了轻量级Mamba UNet(LightM-UNet),它将Mamba和UNet集成在一个轻量级框架中。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...
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…
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...
LightM-UNet首先使用深度卷积(DWConv)层进行浅层特征提取,生成浅层特征图,其中32表示固定数量的滤波器。随后,LightM-UNet结合三个连续的编码器块来提取图像的深度特征。每个编码器块之后,特征图中的通道数翻倍,而分辨率减半。因此,LightM-UNet在第个编码器块提取深度特征,其中。之后,LightM-UNet使用瓶颈块来模拟长...
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...
Open 我以以下代码方式尝试运行代码,能够输出结果,但是会有指针错误,错误信息如下图: import torch from LightMUNet import LightMUNet model = LightMUNet( spatial_dims = 32, init_filters = 32, in_channels=3, out_channels=1, blocks_down=[1, 2, 2, 4], blocks_up=[1, 1, 1], ) model.cu...
LightM-UNet/lightm-unet/nnunetv2/nets/LightMUNet.py Lines 17 to 24 in b484335 def get_dwconv_layer( spatial_dims: int, in_channels: int, out_channels: int, kernel_size: int = 3, stride: int = 1, bias: bool = False ): depth_conv = Convolu...
Building upon this, we employ Mamba as a lightweight substitute for CNN and Transformer within UNet, aiming at tackling challenges stemming from computational resource limitations in real medical settings. To this end, we introduce the Lightweight Mamba UNet (LightM-UNet) that integrates Mamba and...
The LightweightUNet model we proposed works very well thanks to a creative network design, adding fake images to the data, and a multimodal training method. These results show that the model has a lot of potential for use in clinical settings.Rai, Hari Mohan...
UNet achieves superior segmentation performance while drastically reducing parameter and computation costs by 116x and 21x, respectively. This highlights the potential of Mamba in facilitating model lightweighting. Our code implementation is publicly available at https://github.com/MrBlankness/LightM-UNet...