Authors: Jiacheng Ruan, Suncheng Xiang

Published on: February 04, 2024

Impact Score: 8.15

Arxiv code: Arxiv:2402.02491

Summary

• What is new: First medical image segmentation model constructed based on the pure State Space Model (SSM) approach.
• Why this is important: CNNs have limitations in long-range modeling, whereas Transformers have high computational complexity.
• What the research proposes: Introduced a U-shape architecture, Vision Mamba UNet (VM-UNet), leveraging SSMs to efficiently model long-range interactions.
• Results: VM-UNet performs competitively on ISIC17, ISIC18, and Synapse datasets for medical image segmentation tasks.

Technical Details

Technological frameworks used: Vision Mamba UNet (VM-UNet)

Models used: State Space Models (SSMs)

Data used: ISIC17, ISIC18, Synapse datasets

Potential Impact

Healthcare imaging, medical diagnostics companies, AI technology providers in the healthcare sector

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