Summary of Affsegnet: Adaptive Feature Fusion Segmentation Network For Microtumors and Multi-organ Segmentation, by Fuchen Zheng et al.
AFFSegNet: Adaptive Feature Fusion Segmentation Network for Microtumors and Multi-Organ Segmentation
by Fuchen Zheng, Xinyi Chen, Xuhang Chen, Haolun Li, Xiaojiao Guo, Weihuang Liu, Chi-Man Pun, Shoujun Zhou
First submitted to arxiv on: 12 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 The Adaptive Semantic Segmentation Network (ASSNet) is a transformer architecture designed for precise medical image segmentation. It addresses the limitation of previous transformer-based methods, which rely on local window attention and struggle to fuse local and global contextual information. ASSNet’s U-shaped encoder-decoder network extracts multi-scale features using shifted window self-attention across five resolutions, which are then propagated to the decoder through skip connections. The encoder also includes an augmented multi-layer perceptron to model long-range dependencies during feature extraction. The AFF decoder incorporates three key components: LRD, MFF, and ASC blocks, which synergistically facilitate the fusion of multi-scale features and capture long-range dependencies while refining object boundaries. ASSNet achieves state-of-the-art results on diverse medical image segmentation tasks, including multi-organ, liver tumor, and bladder tumor segmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary ASSNet is a new way to segment images in medicine. It’s better than previous methods because it can use information from the whole image, not just small parts. This helps it find tiny tumors and organs more accurately. The network has two main parts: an encoder that looks at the image in different ways, and a decoder that uses this information to draw the boundaries of what it’s looking for. ASSNet is good at finding different things in images, like multiple organs or tumors. |
Keywords
» Artificial intelligence » Attention » Decoder » Encoder » Encoder decoder » Feature extraction » Image segmentation » Self attention » Semantic segmentation » Transformer
