CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer

• URL: http://arxiv.org/abs/2409.07092v1
• Date: Wed, 11 Sep 2024 08:26:28 GMT
• Title: CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer
• Authors: Feiyang Jia, Zhineng Chen, Ziying Song, Lin Liu, Caiyan Jia,
• Abstract summary: Super-resolution (SR) aims to enhance the quality of low-resolution images and has been widely applied in medical imaging. We propose a novel network called CWT-Net, which leverages cross-scale image wavelet transform and Transformer architecture. Our model significantly outperforms state-of-the-art methods in both performance and visualization evaluations.
• Score: 15.930878163092983
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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