Summary of Sfb-net For Cardiac Segmentation: Bridging the Semantic Gap with Attention, by Nicolas Portal (su) et al.
SFB-net for cardiac segmentation: Bridging the semantic gap with attention
by Nicolas Portal, Nadjia Kachenoura, Thomas Dietenbeck, Catherine Achard
First submitted to arxiv on: 24 Oct 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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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 This paper introduces a novel deep learning architecture, called Swin Filtering Block network (SFB-net), designed to address limitations in cardiac image segmentation. The SFB-net combines conventional convolutional layers with swin transformer layers to model long-range dependencies and extract contextual information. Experimental results on the ACDC and M&M’s datasets demonstrate the effectiveness of this approach, achieving an average Dice score of 92.4 and 87.99 respectively, outperforming previous works. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The SFB-net is a deep learning algorithm designed to improve cardiac image segmentation. It uses a combination of convolutional layers and swin transformer layers to analyze images. This helps the algorithm understand the relationships between different parts of the image. The results show that this approach works well, achieving good scores on two different datasets. |
Keywords
» Artificial intelligence » Deep learning » Image segmentation » Transformer
