CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915613866196992 |
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| author | Jia, Feiyang Chen, Zhineng Song, Ziying Liu, Lin Jia, Caiyan |
| author_facet | Jia, Feiyang Chen, Zhineng Song, Ziying Liu, Lin Jia, Caiyan |
| contents | Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging. We found that the design principles of most existing methods are influenced by SR tasks based on real-world images and do not take into account the significance of the multi-level structure in pathological images, even if they can achieve respectable objective metric evaluations. In this work, we delve into two super-resolution working paradigms and propose a novel network called CWT-Net, which leverages cross-scale image wavelet transform and Transformer architecture. Our network consists of two branches: one dedicated to learning super-resolution and the other to high-frequency wavelet features. To generate high-resolution histopathology images, the Transformer module shares and fuses features from both branches at various stages. Notably, we have designed a specialized wavelet reconstruction module to effectively enhance the wavelet domain features and enable the network to operate in different modes, allowing for the introduction of additional relevant information from cross-scale images. Our experimental results demonstrate that our model significantly outperforms state-of-the-art methods in both performance and visualization evaluations and can substantially boost the accuracy of image diagnostic networks. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_07092 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer Jia, Feiyang Chen, Zhineng Song, Ziying Liu, Lin Jia, Caiyan Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging. We found that the design principles of most existing methods are influenced by SR tasks based on real-world images and do not take into account the significance of the multi-level structure in pathological images, even if they can achieve respectable objective metric evaluations. In this work, we delve into two super-resolution working paradigms and propose a novel network called CWT-Net, which leverages cross-scale image wavelet transform and Transformer architecture. Our network consists of two branches: one dedicated to learning super-resolution and the other to high-frequency wavelet features. To generate high-resolution histopathology images, the Transformer module shares and fuses features from both branches at various stages. Notably, we have designed a specialized wavelet reconstruction module to effectively enhance the wavelet domain features and enable the network to operate in different modes, allowing for the introduction of additional relevant information from cross-scale images. Our experimental results demonstrate that our model significantly outperforms state-of-the-art methods in both performance and visualization evaluations and can substantially boost the accuracy of image diagnostic networks. |
| title | CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.07092 |