  Traditionally for improving the segmentation performance of models, most
approaches prefer to use adding more complex modules. And this is not suitable
for the medical field, especially for mobile medical devices, where
computationally loaded models are not suitable for real clinical environments
due to computational resource constraints. Recently, state-space models (SSMs),
represented by Mamba, have become a strong competitor to traditional CNNs and
Transformers. In this paper, we deeply explore the key elements of parameter
influence in Mamba and 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, which achieves excellent
performance with the lowest computational load while keeping the overall number
of processing channels constant. We conducted comparisons and ablation
experiments with several state-of-the-art lightweight models on three skin
lesion public datasets and demonstrated that the UltraLight VM-UNet exhibits
the same strong performance competitiveness with parameters of only 0.049M and
GFLOPs of 0.060. In addition, this study deeply explores the key elements of
parameter influence in Mamba, which will lay a theoretical foundation for Mamba
to possibly become a new mainstream module for lightweighting in the future.
The code is available from https://github.com/wurenkai/UltraLight-VM-UNet .
