Core Concepts
State-of-the-art VM-UNET-V2 model enhances medical image segmentation using Visual State Space Models.
Abstract
The article introduces VM-UNET-V2, a novel approach to medical image segmentation that combines the strengths of State Space Models (SSMs) and UNet architecture. By leveraging Visual State Space (VSS) blocks and Semantics and Detail Infusion (SDI), the model efficiently captures extensive contextual information and infuses semantic details for improved segmentation results. Extensive experiments on various public datasets demonstrate the competitive performance of VM-UNET-V2 in medical image segmentation tasks. The model's linear computational complexity, inspired by Mamba architecture, offers efficient long-range interaction modeling without sacrificing performance.
Key Points:
Introduction to medical image segmentation importance.
Comparison of CNNs and Transformers in medical image segmentation.
Introduction of State Space Models like Mamba for improved performance.
Description of Vision Mamba UNetV2 architecture with VSS blocks and SDI module.
Detailed explanation of Encoder, Decoder, VSS Block, SDI Block, and Loss function.
Results from experiments on skin disease and polyp datasets showcasing competitive performance.
Complexity analysis highlighting superior FLOPs, Params, and FPS of VM-UNET-V2.
Stats
State Space Models (SSMs) - Linear computational complexity demonstrated by Mamba model.
Quotes
"VM-UNetV2 exhibits competitive performance in medical image segmentation tasks."
"We proposed VM-UnetV2 to explore better SSM-based algorithms in medical image segmentation."