MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation

• URL: http://arxiv.org/abs/2412.01405v1
• Date: Mon, 02 Dec 2024 11:49:49 GMT
• Title: MambaU-Lite: A Lightweight Model based on Mamba and Integrated Channel-Spatial Attention for Skin Lesion Segmentation
• Authors: Thi-Nhu-Quynh Nguyen, Quang-Huy Ho, Duy-Thai Nguyen, Hoang-Minh-Quang Le, Van-Truong Pham, Thi-Thao Tran,
• Abstract summary: Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundaries of individual lesions. We introduce a novel lightweight model called MambaU-Lite, featuring just over 400K parameters and a computational cost of more than 1G flops.
• Score: 1.8076316708864304
• License:
• Abstract: Early detection of skin abnormalities plays a crucial role in diagnosing and treating skin cancer. Segmentation of affected skin regions using AI-powered devices is relatively common and supports the diagnostic process. However, achieving high performance remains a significant challenge due to the need for high-resolution images and the often unclear boundaries of individual lesions. At the same time, medical devices require segmentation models to have a small memory foot-print and low computational cost. Based on these requirements, we introduce a novel lightweight model called MambaU-Lite, which combines the strengths of Mamba and CNN architectures, featuring just over 400K parameters and a computational cost of more than 1G flops. To enhance both global context and local feature extraction, we propose the P-Mamba block, a novel component that incorporates VSS blocks along-side multiple pooling layers, enabling the model to effectively learn multiscale features and enhance segmentation performance. We evaluate the model's performance on two skin datasets, ISIC2018 and PH2, yielding promising results. Our source code will be made publicly available at: https://github.com/nqnguyen812/MambaU-Lite.

Related papers

• MambaHSI: Spatial-Spectral Mamba for Hyperspectral Image Classification [46.111607032455225]
  We propose a novel HSI classification model based on a Mamba model, named MambaHSI. Specifically, we design a spatial Mamba block (SpaMB) to model the long-range interaction of the whole image at the pixel-level. We propose a spectral Mamba block (SpeMB) to split the spectral vector into multiple groups, mine the relations across different spectral groups, and extract spectral features.
  arXiv  Detail & Related papers  (2025-01-09T03:27:47Z)
• Mamba-SEUNet: Mamba UNet for Monaural Speech Enhancement [54.427965535613886]
  Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer vision. In this work, we introduce Mamba-SEUNet, an innovative architecture that integrates Mamba with U-Net for SE tasks.
  arXiv  Detail & Related papers  (2024-12-21T13:43:51Z)
• MobileMamba: Lightweight Multi-Receptive Visual Mamba Network [51.33486891724516]
  Previous research on lightweight models has primarily focused on CNNs and Transformer-based designs. We propose the MobileMamba framework, which balances efficiency and performance. MobileMamba achieves up to 83.6% on Top-1, surpassing existing state-of-the-art methods.
  arXiv  Detail & Related papers  (2024-11-24T18:01:05Z)
• LaMamba-Diff: Linear-Time High-Fidelity Diffusion Models Based on Local Attention and Mamba [54.85262314960038]
  Local Attentional Mamba blocks capture both global contexts and local details with linear complexity. Our model exhibits exceptional scalability and surpasses the performance of DiT across various model scales on ImageNet at 256x256 resolution. Compared to state-of-the-art diffusion models on ImageNet 256x256 and 512x512, our largest model presents notable advantages, such as a reduction of up to 62% GFLOPs.
  arXiv  Detail & Related papers  (2024-08-05T16:39:39Z)
• MambaVision: A Hybrid Mamba-Transformer Vision Backbone [54.965143338206644]
  We propose a novel hybrid Mamba-Transformer backbone, denoted as MambaVision, which is specifically tailored for vision applications. Our core contribution includes redesigning the Mamba formulation to enhance its capability for efficient modeling of visual features. We conduct a comprehensive ablation study on the feasibility of integrating Vision Transformers (ViT) with Mamba.
  arXiv  Detail & Related papers  (2024-07-10T23:02:45Z)
• UltraLight VM-UNet: Parallel Vision Mamba Significantly Reduces Parameters for Skin Lesion Segmentation [2.0555786400946134]
  State-space models (SSMs) have become a strong competitor to traditional CNNs and Transformers. We propose an UltraLight Vision Mamba UNet (UltraLight VM-UNet) based on this. Specifically, we propose a method for processing features in parallel Vision Mamba, named PVM Layer.
  arXiv  Detail & Related papers  (2024-03-29T08:03:42Z)
• Integrating Mamba Sequence Model and Hierarchical Upsampling Network for Accurate Semantic Segmentation of Multiple Sclerosis Legion [0.0]
  We introduce Mamba HUNet, a novel architecture tailored for robust and efficient segmentation tasks. We first converted HUNet into a lighter version, maintaining performance parity and then integrated this lighter HUNet into Mamba HUNet, further enhancing its efficiency. Experimental results on publicly available Magnetic Resonance Imaging scans, notably in Multiple Sclerosis lesion segmentation, demonstrate Mamba HUNet's effectiveness across diverse segmentation tasks.
  arXiv  Detail & Related papers  (2024-03-26T06:57:50Z)
• 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)
• 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)
• MALUNet: A Multi-Attention and Light-weight UNet for Skin Lesion Segmentation [13.456935850832565]
  We propose a light-weight model to achieve competitive performances for skin lesion segmentation at the lowest cost of parameters and computational complexity. We combine four modules with our U-shape architecture and obtain a light-weight medical image segmentation model dubbed as MALUNet. Compared with UNet, our model improves the mIoU and DSC metrics by 2.39% and 1.49%, respectively, with a 44x and 166x reduction in the number of parameters and computational complexity.
  arXiv  Detail & Related papers  (2022-11-03T13:19:22Z)

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.