Summary of Lssf-net: Lightweight Segmentation with Self-awareness, Spatial Attention, and Focal Modulation, by Hamza Farooq et al.
LSSF-Net: Lightweight Segmentation with Self-Awareness, Spatial Attention, and Focal Modulation
by Hamza Farooq, Zuhair Zafar, Ahsan Saadat, Tariq M Khan, Shahzaib Iqbal, Imran Razzak
First submitted to arxiv on: 3 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 Accurate segmentation of skin lesions within dermoscopic images is crucial for computer-aided diagnosis on mobile platforms. The task is challenging due to varying lesion shapes, lack of defined edges, and obstructions such as hair strands and marker colors. Skin lesions often exhibit subtle texture and color variations that are difficult to differentiate from surrounding healthy skin, requiring models that capture both fine-grained details and broader contextual information. Current melanoma segmentation models based on fully connected networks and U-Nets struggle with capturing complex characteristics like indistinct boundaries and diverse lesion appearances, leading to suboptimal segmentation. To address these challenges, a novel lightweight network is proposed for skin lesion segmentation utilizing mobile devices, featuring an encoder-decoder architecture with conformer-based focal modulation attention, self-aware local and global spatial attention, and split channel-shuffle. The model achieves state-of-the-art performance on four well-established benchmark datasets: ISIC 2016, ISIC 2017, ISIC 2018, and PH2, reflected in a high Jaccard index. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to detect skin cancer from pictures taken with a smartphone. This is hard because the skin lesions (the areas where cancer might be) can look very different and are often blurry or hidden by hair or markers. To make it easier, scientists created a new way for computers to analyze these images using a special algorithm that looks at both tiny details and big pictures. They tested this method on four sets of images and found that it works better than other methods in detecting skin cancer. |
Keywords
» Artificial intelligence » Attention » Encoder decoder
