metadata
license: apache-2.0
tags:
- medical-image-segmentation
- mamba
- unet
- pytorch
datasets:
- synapse
LGFVM-UNet — Synapse Multi-organ Segmentation
Implementation of "A Local-Global Fusion Vision Mamba UNet Framework for Medical Image Segmentation" published in Engineering Applications of Artificial Intelligence (2026).
Model Description
LGFVM-UNet combines:
- LGF-VSS block: parallel multi-scale convolutions + Mamba SSM fused via QuadGate dynamic gating
- MCFB: Multi-level Cross-scale Feature Fusion Block with spatial-channel dual attention
- Adaptive Hierarchical Loss: gradient statistics-based dynamic supervision weighting
Performance on Synapse Multi-organ Dataset
| Method | DSC (%) ↑ | HD95 ↓ |
|---|---|---|
| UNet | 71.98 | 22.39 |
| TransUNet | 82.42 | 25.54 |
| MSVM-UNet | 87.41 | 8.91 |
| LGFVM-UNet (paper) | 88.74 | 6.65 |
Training Config
- Optimizer: AdamW (lr=1e-4, weight_decay=1e-4)
- Batch size: 32
- Epochs: 200 (early stopping patience=15)
- Scheduler: CosineAnnealingLR
- Input size: 224×224
- GPU: NVIDIA RTX 4090
Usage
import torch
from models.vmunet.vmunet import LGFVMUNet
model = LGFVMUNet(
num_classes=9,
input_channels=3,
depths=[2, 2, 2, 2],
depths_decoder=[2, 2, 2, 1],
drop_path_rate=0,
use_full_scale_skip=True,
)
checkpoint = torch.load("best_model.pth", map_location="cpu")
model.load_state_dict(checkpoint)
model.eval()
# Input: (B, 3, 224, 224)
# Output: (B, 9, 224, 224) logits
with torch.no_grad():
logits, _ = model(image)
pred = torch.argmax(torch.softmax(logits, dim=1), dim=1)
Citation
@article{li2026lgfvmunet,
title={A Local-Global Fusion Vision Mamba UNet Framework for medical image segmentation},
author={Li, Yanbo and Mao, Zihan and Qin, Feiwei and Peng, Yong and Zhang, Guodao and Xi, Xugang and Ma, Xiaoqin and Yu, Huanhuan and Zhou, Yu and Zhu, Zhu},
journal={Engineering Applications of Artificial Intelligence},
volume={169},
pages={113987},
year={2026},
publisher={Elsevier}
}