Summary of Awgunet: Attention-aided Wavelet Guided U-net For Nuclei Segmentation in Histopathology Images, by Ayush Roy et al.
AWGUNET: Attention-Aided Wavelet Guided U-Net for Nuclei Segmentation in Histopathology Images
by Ayush Roy, Payel Pramanik, Dmitrii Kaplun, Sergei Antonov, Ram Sarkar
First submitted to arxiv on: 12 Jun 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 to nuclei segmentation in histopathological images is presented, combining the U-Net architecture with a DenseNet-121 backbone. The proposed model incorporates Wavelet-guided channel attention and learnable weighted global attention modules to enhance cell boundary delineation and channel-specific attention. Additionally, a decoder module refines segmentation by handling staining patterns. Experimental results on two publicly accessible datasets, Monuseg and TNBC, demonstrate the superiority of the proposed model, highlighting its potential to advance histopathological image analysis and cancer diagnosis. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Nuclei segmentation in histopathological images is important for accurate cancer diagnosis. Currently, this process relies heavily on manual annotation by clinical experts. However, automation can provide valuable support. This paper presents a new approach that combines U-Net architecture with DenseNet-121 backbone to capture contextual and spatial information. The model also includes special modules to help identify cell boundaries and handle different staining patterns. Results are shown on two public datasets, proving the model’s effectiveness. |
Keywords
» Artificial intelligence » Attention » Decoder
