# EM-Net: Efficient Channel and Frequency Learning with Mamba for 3D Medical Image Segmentation

arXiv: https://arxiv.org/abs/2409.17675 · HTML: https://mlsanity.com/paper/2409.17675/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Placed in Mamba for medical image analysis, one of 10 directions in State-space sequence models (S4, Mamba).
- Builds on 17 of its 21 references that are in the map.
- First posted on arXiv 26 Sep 2024.

## Placed in

- State-space sequence models (S4, Mamba) / [Mamba for medical image analysis](https://mlsanity.com/ssm/mamba-for-medical-image-analysis/)

## Builds on (most cited first, 17 of 17)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Mamba: Linear-Time Sequence Modeling with Selective State Spaces](https://mlsanity.com/paper/2312.00752/) (2023)
- [Efficiently Modeling Long Sequences with Structured State Spaces](https://mlsanity.com/paper/2111.00396/) (2021)
- [3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation](https://mlsanity.com/paper/1606.06650/) (2016)
- [Vision Mamba: Efficient Visual Representation Learning with Bidirectional State Space Model](https://mlsanity.com/paper/2401.09417/) (2024)
- [VMamba: Visual State Space Model](https://mlsanity.com/paper/2401.10166/) (2024)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [Combining Recurrent, Convolutional, and Continuous-time Models with Linear State-Space Layers](https://mlsanity.com/paper/2110.13985/) (2021)
- [U-Mamba: Enhancing Long-range Dependency for Biomedical Image Segmentation](https://mlsanity.com/paper/2401.04722/) (2024)
- [3D MRI brain tumor segmentation using autoencoder regularization](https://mlsanity.com/paper/1810.11654/) (2018)
- [3D UX-Net: A Large Kernel Volumetric ConvNet Modernizing Hierarchical Transformer for Medical Image Segmentation](https://mlsanity.com/paper/2209.15076/) (2022)
- [A Robust Volumetric Transformer for Accurate 3D Tumor Segmentation](https://mlsanity.com/paper/2111.13300/) (2021)
- [Diff-UNet: A Diffusion Embedded Network for Volumetric Segmentation](https://mlsanity.com/paper/2303.10326/) (2023)
- [3D TransUNet: Advancing Medical Image Segmentation through Vision Transformers](https://mlsanity.com/paper/2310.07781/) (2023)
- [FreMIM: Fourier Transform Meets Masked Image Modeling for Medical Image Segmentation](https://mlsanity.com/paper/2304.10864/) (2023)

## Built on by (most cited first, 12 of 12)

- [A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2503.19308/) (2025)
- [Universal Vessel Segmentation for Multi-Modality Retinal Images](https://mlsanity.com/paper/2502.06987/) (2025)
- [Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation](https://mlsanity.com/paper/2507.06363/) (2025)
- [SparseMamba-PCL: Scribble-Supervised Medical Image Segmentation via SAM-Guided Progressive Collaborative Learning](https://mlsanity.com/paper/2503.01633/) (2025)
- [Self is the Best Learner: CT-free Ultra-Low-Dose PET Organ Segmentation via Collaborating Denoising and Segmentation Learning](https://mlsanity.com/paper/2503.03786/) (2025)
- [FaRMamba: Frequency-based learning and Reconstruction aided Mamba for Medical Segmentation](https://mlsanity.com/paper/2507.20056/) (2025)
- [SSFMamba: Learning Symmetry-driven Spatial-Frequency Modeling for Physically Consistent 3D Medical Image Segmentation](https://mlsanity.com/paper/2508.03069/) (2025)
- [MSD-KMamba: Bidirectional Spatial-Aware Multi-Modal 3D Brain Segmentation via Multi-scale Self-Distilled Fusion Strategy](https://mlsanity.com/paper/2509.23677/) (2025)
- [TSkel-Mamba: Temporal Dynamic Modeling via State Space Model for Human Skeleton-based Action Recognition](https://mlsanity.com/paper/2512.11503/) (2025)
- [SwinTF3D: A Lightweight Multimodal Fusion Approach for Text-Guided 3D Medical Image Segmentation](https://mlsanity.com/paper/2512.22878/) (2025)
- [Benchmarking CNN-based Models against Transformer-based Models for Abdominal Multi-Organ Segmentation on the RATIC Dataset](https://mlsanity.com/paper/2603.18616/) (2026)
- [TEAMS: Text-prompted spatiotEmporal dual-heAd Mamba Snake](https://mlsanity.com/paper/2608.17421/) (2026)

## Abstract (arXiv)

Convolutional neural networks have primarily led 3D medical image segmentation but may be limited by small receptive fields. Transformer models excel in capturing global relationships through self-attention but are challenged by high computational costs at high resolutions. Recently, Mamba, a state space model, has emerged as an effective approach for sequential modeling. Inspired by its success, we introduce a novel Mamba-based 3D medical image segmentation model called EM-Net. It not only efficiently captures attentive interaction between regions by integrating and selecting channels, but also effectively utilizes frequency domain to harmonize the learning of features across varying scales, while accelerating training speed. Comprehensive experiments on two challenging multi-organ datasets with other state-of-the-art (SOTA) algorithms show that our method exhibits better segmentation accuracy while requiring nearly half the parameter size of SOTA models and 2x faster training speed.