SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image
Segmentation
- URL: http://arxiv.org/abs/2401.13560v3
- Date: Sun, 25 Feb 2024 14:45:06 GMT
- Title: SegMamba: Long-range Sequential Modeling Mamba For 3D Medical Image
Segmentation
- Authors: Zhaohu Xing, Tian Ye, Yijun Yang, Guang Liu, Lei Zhu
- Abstract summary: We introduce SegMamba, a novel 3D medical image textbfSegmentation textbfMamba model.
SegMamba excels in whole volume feature modeling from a state space model standpoint.
Experiments on the BraTS2023 dataset demonstrate the effectiveness and efficiency of our SegMamba.
- Score: 17.676472608152704
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: The Transformer architecture has shown a remarkable ability in modeling
global relationships. However, it poses a significant computational challenge
when processing high-dimensional medical images. This hinders its development
and widespread adoption in this task. Mamba, as a State Space Model (SSM),
recently emerged as a notable manner for long-range dependencies in sequential
modeling, excelling in natural language processing filed with its remarkable
memory efficiency and computational speed. Inspired by its success, we
introduce SegMamba, a novel 3D medical image \textbf{Seg}mentation
\textbf{Mamba} model, designed to effectively capture long-range dependencies
within whole volume features at every scale. Our SegMamba, in contrast to
Transformer-based methods, excels in whole volume feature modeling from a state
space model standpoint, maintaining superior processing speed, even with volume
features at a resolution of {$64\times 64\times 64$}. Comprehensive experiments
on the BraTS2023 dataset demonstrate the effectiveness and efficiency of our
SegMamba. The code for SegMamba is available at:
https://github.com/ge-xing/SegMamba
Related papers
- DeciMamba: Exploring the Length Extrapolation Potential of Mamba [89.07242846058023]
We introduce DeciMamba, a context-extension method specifically designed for Mamba.
We show that DeciMamba can extrapolate context lengths 25x longer than the ones seen during training, and does so without utilizing additional computational resources.
arXiv Detail & Related papers (2024-06-20T17:40:18Z) - ReMamber: Referring Image Segmentation with Mamba Twister [51.291487576255435]
ReMamber is a novel RIS architecture that integrates the power of Mamba with a multi-modal Mamba Twister block.
The Mamba Twister explicitly models image-text interaction, and fuses textual and visual features through its unique channel and spatial twisting mechanism.
arXiv Detail & Related papers (2024-03-26T16:27:37Z) - ZigMa: A DiT-style Zigzag Mamba Diffusion Model [23.581004543220622]
We aim to leverage the long sequence modeling capability of a State-Space Model called Mamba to extend its applicability to visual data generation.
We introduce a simple, plug-and-play, zero- parameter method named Zigzag Mamba, which outperforms Mamba-based baselines.
We integrate Zigzag Mamba with Interpolant framework to investigate the scalability of the model on large-resolution visual datasets.
arXiv Detail & Related papers (2024-03-20T17:59:14Z) - LKM-UNet: Large Kernel Vision Mamba UNet for Medical Image Segmentation [9.862277278217045]
In this paper, we introduce a Large Kernel Vision Mamba U-shape Network, or LKM-UNet, for medical image segmentation.
A distinguishing feature of our LKM-UNet is its utilization of large Mamba kernels, excelling in locally spatial modeling compared to small kernel-based CNNs and Transformers.
Comprehensive experiments demonstrate the feasibility and the effectiveness of using large-size Mamba kernels to achieve large receptive fields.
arXiv Detail & Related papers (2024-03-12T05:34:51Z) - LightM-UNet: Mamba Assists in Lightweight UNet for Medical Image
Segmentation [10.563051220050035]
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.
Experiments conducted on two real-world 2D/3D datasets demonstrate that LightM-UNet surpasses existing state-of-the-art literature.
arXiv Detail & Related papers (2024-03-08T12:07:42Z) - MiM-ISTD: Mamba-in-Mamba for Efficient Infrared Small Target Detection [72.46396769642787]
We develop a nested structure, Mamba-in-Mamba (MiM-ISTD), for efficient infrared small target detection.
MiM-ISTD is $8 times$ faster than the SOTA method and reduces GPU memory usage by 62.2$%$ when testing on $2048 times 2048$ images.
arXiv Detail & Related papers (2024-03-04T15:57:29Z) - PointMamba: A Simple State Space Model for Point Cloud Analysis [65.59944745840866]
We propose PointMamba, transferring the success of Mamba, a recent representative state space model (SSM), from NLP to point cloud analysis tasks.
Unlike traditional Transformers, PointMamba employs a linear complexity algorithm, presenting global modeling capacity while significantly reducing computational costs.
arXiv Detail & Related papers (2024-02-16T14:56:13Z) - Mamba-UNet: UNet-Like Pure Visual Mamba for Medical Image Segmentation [21.1787366866505]
We propose Mamba-UNet, a novel architecture that synergizes the U-Net in medical image segmentation with Mamba's capability.
Mamba-UNet adopts a pure Visual Mamba (VMamba)-based encoder-decoder structure, infused with skip connections to preserve spatial information across different scales of the network.
arXiv Detail & Related papers (2024-02-07T18:33:04Z) - Swin-UMamba: Mamba-based UNet with ImageNet-based pretraining [85.08169822181685]
This paper introduces a novel Mamba-based model, Swin-UMamba, designed specifically for medical image segmentation tasks.
Swin-UMamba demonstrates superior performance with a large margin compared to CNNs, ViTs, and latest Mamba-based models.
arXiv Detail & Related papers (2024-02-05T18:58:11Z) - BlackMamba: Mixture of Experts for State-Space Models [10.209192169793772]
State-space models (SSMs) have recently demonstrated competitive performance to transformers at large-scale language modeling benchmarks.
MoE models have shown remarkable performance while significantly reducing the compute and latency costs of inference.
We present BlackMamba, a novel architecture that combines the Mamba SSM with MoE to obtain the benefits of both.
arXiv Detail & Related papers (2024-02-01T07:15:58Z) - MambaByte: Token-free Selective State Space Model [71.90159903595514]
MambaByte is a token-free adaptation of the Mamba SSM trained autoregressively on byte sequences.
We show MambaByte to be competitive with, and even to outperform, state-of-the-art subword Transformers on language modeling tasks.
arXiv Detail & Related papers (2024-01-24T18:53:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.