Towards Arbitrary-Scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework via Implicit Self-texture Enhancement

• URL: http://arxiv.org/abs/2401.15613v6
• Date: Mon, 15 Jul 2024 08:24:59 GMT
• Title: Towards Arbitrary-Scale Histopathology Image Super-resolution: An Efficient Dual-branch Framework via Implicit Self-texture Enhancement
• Authors: Minghong Duan, Linhao Qu, Zhiwei Yang, Manning Wang, Chenxi Zhang, Zhijian Song,
• Abstract summary: We propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale super-resolution of pathology images. ISTE contains a pixel learning branch and a texture learning branch, which first learn pixel features and texture features, respectively. We show that ISTE outperforms existing fixed-scale and arbitrary-scale algorithms at multiple magnifications.
• Score: 18.881480825169053
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: High-quality whole-slide scanners are expensive, complex, and time-consuming, thus limiting the acquisition and utilization of high-resolution pathology whole-slide images in daily clinical work. Deep learning-based single-image super-resolution techniques are an effective way to solve this problem by synthesizing high-resolution images from low-resolution ones. However, the existing super-resolution models applied in pathology images can only work in fixed integer magnifications, significantly decreasing their applicability. Though methods based on implicit neural representation have shown promising results in arbitrary-scale super-resolution of natural images, applying them directly to pathology images is inadequate because they have unique fine-grained image textures different from natural images. Thus, we propose an Implicit Self-Texture Enhancement-based dual-branch framework (ISTE) for arbitrary-scale super-resolution of pathology images to address this challenge. ISTE contains a pixel learning branch and a texture learning branch, which first learn pixel features and texture features, respectively. Then, we design a two-stage texture enhancement strategy to fuse the features from the two branches to obtain the super-resolution results, where the first stage is feature-based texture enhancement, and the second stage is spatial-domain-based texture enhancement. Extensive experiments on three public datasets show that ISTE outperforms existing fixed-scale and arbitrary-scale algorithms at multiple magnifications and helps to improve downstream task performance. To the best of our knowledge, this is the first work to achieve arbitrary-scale super-resolution in pathology images. Codes will be available.

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