# CMUNeXt: An Efficient Medical Image Segmentation Network based on Large Kernel and Skip Fusion

arXiv: https://arxiv.org/abs/2308.01239 · HTML: https://mlsanity.com/paper/2308.01239/

## Key facts

- Cited by 31 papers in the map (data through 14 Oct 2026).
- Builds on 20 of its 24 references that are in the map.
- First posted on arXiv 2 Aug 2023.

## Builds on (most cited first, 20 of 20)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation](https://mlsanity.com/paper/2102.04306/) (2021)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Attention U-Net: Learning Where to Look for the Pancreas](https://mlsanity.com/paper/1804.03999/) (2018)
- [Swin-Unet: Unet-like Pure Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2105.05537/) (2021)
- [MobileViT: Light-weight, General-purpose, and Mobile-friendly Vision Transformer](https://mlsanity.com/paper/2110.02178/) (2021)
- [UNet++: Redesigning Skip Connections to Exploit Multiscale Features in Image Segmentation](https://mlsanity.com/paper/1912.05074/) (2019)
- [UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation](https://mlsanity.com/paper/2004.08790/) (2020)
- [Medical Transformer: Gated Axial-Attention for Medical Image Segmentation](https://mlsanity.com/paper/2102.10662/) (2021)
- [Patches Are All You Need?](https://mlsanity.com/paper/2201.09792/) (2022)
- [UNeXt: MLP-based Rapid Medical Image Segmentation Network](https://mlsanity.com/paper/2203.04967/) (2022)
- [BUSIS: A Benchmark for Breast Ultrasound Image Segmentation](https://mlsanity.com/paper/1801.03182/) (2018)
- [CMU-Net: A Strong ConvMixer-based Medical Ultrasound Image Segmentation Network](https://mlsanity.com/paper/2210.13012/) (2022)
- [Global Guidance Network for Breast Lesion Segmentation in Ultrasound Images](https://mlsanity.com/paper/2104.01896/) (2021)

## Built on by (most cited first, 30 of 31)

- [MK-UNet: Multi-kernel Lightweight CNN for Medical Image Segmentation](https://mlsanity.com/paper/2509.18493/) (2025)
- [Pre-Trained LLM is a Semantic-Aware and Generalizable Segmentation Booster](https://mlsanity.com/paper/2506.18034/) (2025)
- [Mobile U-ViT: Revisiting large kernel and U-shaped ViT for efficient medical image segmentation](https://mlsanity.com/paper/2508.01064/) (2025)
- [Catheter Detection and Segmentation in X-ray Images via Multi-task Learning](https://mlsanity.com/paper/2503.02717/) (2025)
- [UltraLBM-UNet: Ultralight Bidirectional Mamba-based Model for Skin Lesion Segmentation](https://mlsanity.com/paper/2512.21584/) (2025)
- [U-RWKV: Lightweight medical image segmentation with direction-adaptive RWKV](https://mlsanity.com/paper/2507.11415/) (2025)
- [MIP Candy: A Modular PyTorch Framework for Medical Image Processing](https://mlsanity.com/paper/2602.21033/) (2026)
- [MSA-UNet3+: Multi-Scale Attention UNet3+ with New Supervised Prototypical Contrastive Loss for Coronary DSA Image Segmentation](https://mlsanity.com/paper/2504.05184/) (2025)
- [CARE: Anti-entanglement Ultrasound Image Segmentation via Channel-Aware Region Extrication](https://mlsanity.com/paper/2508.13899/) (2025)
- [LGMSNet: Thinning a medical image segmentation model via dual-level multiscale fusion](https://mlsanity.com/paper/2508.15476/) (2025)
- [LoMix: Learnable Weighted Multi-Scale Logits Mixing for Medical Image Segmentation](https://mlsanity.com/paper/2510.22995/) (2025)
- [Inspecting Model Fairness in Ultrasound Segmentation Tasks](https://mlsanity.com/paper/2312.02501/) (2023)
- [ScaleFusionNet: Transformer-Guided Multi-Scale Feature Fusion for Skin Lesion Segmentation](https://mlsanity.com/paper/2503.03327/) (2025)
- [DyGLNet: Hybrid Global-Local Feature Fusion with Dynamic Upsampling for Medical Image Segmentation](https://mlsanity.com/paper/2509.12763/) (2025)
- [CephRes-MHNet: A Multi-Head Residual Network for Accurate and Robust Cephalometric Landmark Detection](https://mlsanity.com/paper/2511.10173/) (2025)
- [Layer-wise Noise Guided Selective Wavelet Reconstruction for Robust Medical Image Segmentation](https://mlsanity.com/paper/2511.16162/) (2025)
- [LAW & ORDER: Adaptive Spatial Weighting for Medical Diffusion and Segmentation](https://mlsanity.com/paper/2603.04795/) (2026)
- [Concept-to-Pixel: Prompt-Free Universal Medical Image Segmentation](https://mlsanity.com/paper/2603.17746/) (2026)
- [MonoUNet: A Robust Tiny Neural Network for Automated Knee Cartilage Segmentation on Point-of-Care Ultrasound Devices](https://mlsanity.com/paper/2604.07780/) (2026)
- [PBE-UNet: A light weight Progressive Boundary-Enhanced U-Net with Scale-Aware Aggregation for Ultrasound Image Segmentation](https://mlsanity.com/paper/2604.13791/) (2026)
- [UHR-Net: An Uncertainty-Aware Hypergraph Refinement Network for Medical Image Segmentation](https://mlsanity.com/paper/2604.28095/) (2026)
- [XTinyU-Net: Training-Free U-Net Scaling via Initialization-Time Sensitivity](https://mlsanity.com/paper/2605.09639/) (2026)
- [Polygon-mamba: Retinal vessel segmentation using polygon scanning mamba and space-frequency collaborative attention](https://mlsanity.com/paper/2605.10581/) (2026)
- [DepthPolyp: Pseudo-Depth Guided Lightweight Segmentation for Real-Time Colonoscopy](https://mlsanity.com/paper/2605.16519/) (2026)
- [HTC-SGA Former: A Hybrid Transformer-CNN Network with Self-Guided Attention and a New Boundary-Weighted Adaptive Loss for Coronary DSA Vessel Segmentation](https://mlsanity.com/paper/2606.29744/) (2026)
- [APRIL-MedSeg: A Modular Medical Image Segmentation Toolbox Embracing Modern Paradigms](https://mlsanity.com/paper/2606.30577/) (2026)
- [Lightweight Polyp Segmentation via a Gain-Aware Prediction-Space Recursive Controller](https://mlsanity.com/paper/2607.03062/) (2026)
- [MoSSGate: Memory-Modulated State-Space Gating for Skin Lesion Segmentation](https://mlsanity.com/paper/2609.20181/) (2026)
- [GAD-MambaUNet: Direction-Group Mamba with Gradient-Adaptive DINOv3 Distillation for Lightweight Medical Image Segmentation](https://mlsanity.com/paper/2609.26729/) (2026)
- [LightMIS: Ultra-Lightweight Medical Image Segmentation Without a Stage-Wise Decoder](https://mlsanity.com/paper/2609.28327/) (2026)

## Abstract (arXiv)

The U-shaped architecture has emerged as a crucial paradigm in the design of medical image segmentation networks. However, due to the inherent local limitations of convolution, a fully convolutional segmentation network with U-shaped architecture struggles to effectively extract global context information, which is vital for the precise localization of lesions. While hybrid architectures combining CNNs and Transformers can address these issues, their application in real medical scenarios is limited due to the computational resource constraints imposed by the environment and edge devices. In addition, the convolutional inductive bias in lightweight networks adeptly fits the scarce medical data, which is lacking in the Transformer based network. In order to extract global context information while taking advantage of the inductive bias, we propose CMUNeXt, an efficient fully convolutional lightweight medical image segmentation network, which enables fast and accurate auxiliary diagnosis in real scene scenarios. CMUNeXt leverages large kernel and inverted bottleneck design to thoroughly mix distant spatial and location information, efficiently extracting global context information. We also introduce the Skip-Fusion block, designed to enable smooth skip-connections and ensure ample feature fusion. Experimental results on multiple medical image datasets demonstrate that CMUNeXt outperforms existing heavyweight and lightweight medical image segmentation networks in terms of segmentation performance, while offering a faster inference speed, lighter weights, and a reduced computational cost. The code is available at https://github.com/FengheTan9/CMUNeXt.