CWT-Net: Super-resolution of Histopathology Images Using a Cross-scale Wavelet-based Transformer
This work addresses the problem of improving image quality for medical diagnosis in pathology, though it appears incremental as it builds on existing super-resolution paradigms with domain-specific adaptations.
The authors tackled super-resolution for histopathology images by proposing CWT-Net, a network that uses cross-scale wavelet transforms and a Transformer to enhance multi-level structures, achieving significant performance gains over state-of-the-art methods and boosting diagnostic network accuracy.
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.