Summary of Mobileunetr: a Lightweight End-to-end Hybrid Vision Transformer For Efficient Medical Image Segmentation, by Shehan Perera et al.
MobileUNETR: A Lightweight End-To-End Hybrid Vision Transformer For Efficient Medical Image Segmentation
by Shehan Perera, Yunus Erzurumlu, Deepak Gulati, Alper Yilmaz
First submitted to arxiv on: 4 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A machine learning-based approach to skin cancer segmentation, called MobileUNETR, is introduced. This method aims to balance local and global contextual feature extraction in an efficient manner, while minimizing model size. The proposed architecture consists of a lightweight hybrid CNN-Transformer encoder, a novel hybrid decoder that utilizes low-level and global features at different resolutions, and achieves superior performance with 3 million parameters and a computational complexity of 1.3 GFLOP, resulting in 10x and 23x reduction in parameters and FLOPS, respectively. The effectiveness of the proposed method is validated through extensive experiments on four publicly available skin lesion segmentation datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary MobileUNETR is a new way to find cancer in pictures of skin. It’s like a super smart doctor that looks at pictures of skin and finds where there might be cancer. This helps doctors make sure they get rid of the bad cells before it spreads. The new method is special because it can look at pictures fast and doesn’t take up too much computer power. This means it could help doctors find cancer faster and more easily. |
Keywords
» Artificial intelligence » Cnn » Decoder » Encoder » Feature extraction » Machine learning » Transformer
