Summary of Ad-net: Attention-based Dilated Convolutional Residual Network with Guided Decoder For Robust Skin Lesion Segmentation, by Asim Naveed et al.
AD-Net: Attention-based dilated convolutional residual network with guided decoder for robust skin lesion segmentation
by Asim Naveed, Syed S. Naqvi, Tariq M. Khan, Shahzaib Iqbal, M. Yaqoob Wani, Haroon Ahmed Khan
First submitted to arxiv on: 9 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 novel approach is proposed for skin lesion segmentation in computer-aided diagnosis tools. The dilated convolutional residual network incorporates an attention-based spatial feature enhancement block (ASFEB) and a guided decoder strategy to improve spatial feature information. Each dilated convolutional residual block employs varying dilation rates, broadening the receptive field. An ASFEB combines features from average and maximum-pooling operations, weighted by global average pooling and convolution outcomes. The guided decoder optimizes each block using an individual loss function, facilitating faster convergence. The proposed AD-Net outperforms peer methods on four public benchmark datasets without data augmentation, verified by a Wilcoxon signed-rank test. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Skin lesion segmentation is crucial for accurate computer-aided diagnosis of skin cancer. Researchers have developed a new method to improve this process. They used a special kind of neural network called a dilated convolutional residual network, which helps the model learn more information about the images it’s looking at. The approach also includes an “attention-based spatial feature enhancement block” that helps the model focus on important parts of the image. This makes the model better at recognizing skin lesions and reduces the need for labeled training data. |
Keywords
» Artificial intelligence » Attention » Data augmentation » Decoder » Loss function » Neural network » Residual network
