AI 中文总结
针对现有超分辨率方法难以保留病理图像细胞形态的问题，本文提出Morph-ISR框架，在TCGA等数据集上实现最优的LPIPS等指标，可高效保留诊断相关细节并支持边缘部署。
AI 中文摘要
数字病理学（Digital Pathology, DP）的精准诊断依赖高分辨率全切片图像，但硬件成本常限制其临床应用。超分辨率（Super-Resolution, SR）通过计算增强低分辨率采集图像，是颇具前景的替代方案。然而现有SR方法常难以保留细粒度细胞形态，在复杂组织变异下出现纹理过平滑、结构边界模糊的问题。为解决该问题，本文提出面向DP的形态感知隐式超分辨率框架Morph-ISR，可亚像素精度恢复诊断相关细节。Morph-ISR将SR重新表述为基于连续坐标的重建问题，集成隐式位置感知核生成器（Implicit Position-aware Kernel Generator, IPKG）以自适应建模空间变化的组织形态；还引入形态保真先验（Morphological Fidelity Prior, MFP），利用预训练细胞分割网络的语义指导，实现保边界、感知区域的重建，提升关键细胞边界与核纹理的表征能力。在TCGA和SurGen数据集上的实验显示，Morph-ISR在评估方法中取得最优的LPIPS和ST-LPIPS，较次优方法分别降低达38.37%和39.55%，同时保持较高的PSNR和SSIM。结果表明，Morph-ISR能出色保留诊断相关的细胞边界与核纹理，且紧凑的参数化设计和高吞吐量支持高效的边缘部署，代码与训练模型将在发表后发布。
英文摘要
Accurate diagnosis in Digital Pathology (DP) relies on high-resolution whole-slide images, yet clinical deployment is often limited by hardware costs. Super-Resolution (SR) offers a promising alternative by computationally enhancing low-resolution acquisitions. However, existing SR methods frequently struggle to preserve fine-grained cellular morphology, leading to texture oversmoothing and blurred structural boundaries under complex tissue variability. To address this issue, we propose Morph-ISR, a morphology-aware implicit super-resolution framework for DP that restores diagnostically relevant details with sub-pixel precision. Morph-ISR reformulates SR as a continuous coordinate-based reconstruction problem and integrates an Implicit Position-aware Kernel Generator (IPKG) to adaptively model spatially varying tissue morphology. To further enhance structural fidelity, a Morphological Fidelity Prior (MFP) is introduced, leveraging semantic guidance from a pre-trained cell segmentation network to enforce boundary-preserving and region-aware reconstruction, thereby improving the representation of critical cellular boundaries and nuclear textures. Experiments on TCGA and SurGen datasets show that Morph-ISR achieves the best LPIPS and ST-LPIPS among the evaluated methods, reducing them by up to 38.37% and 39.55%, respectively, over the second-best methods while maintaining strong PSNR and SSIM. These results demonstrate superior preservation of diagnostically relevant cellular boundaries and nuclear textures, while compact parameterization and high throughput support efficient edge deployment. Code and trained models will be released upon publication.
