基于提示混合的病理图像复原
Pathology Image Restoration via Mixture of Prompts
AI总结：
本文提出级联 transformer 与扩散模型的两阶段病理图像复原方法，并融合散焦、病理基础模型语义及边缘结构提示，实现从单焦平面扫描复原高质量图像。
摘要：
在数字病理学中，获取全焦清晰图像对于高质量成像和高效临床工作流程至关重要。传统扫描仪通过在不同深度的多个焦平面上扫描，然后将其合并来实现这一点，但这种方式相对缓慢，并且常常难以处理复杂的组织散焦。近期流行的图像复原技术提供了一种从单焦平面扫描复原高质量病理图像的途径。然而，由于病理图像中复杂的散焦模式及其特定领域的语义复杂性，现有图像复原方法仍显不足。在本研究中，我们设计了一种级联 transformer 与扩散模型的两阶段复原方案，以分别利用它们在保持图像保真度和感知质量方面的能力。我们特别为该两阶段方案提出了一种新颖的提示混合。在对显微成像中的散焦进行建模的初始提示基础上，我们设计了两个提示：一个来自病理基础模型，用于描述高层图像语义；另一个通过边缘提取描述细粒度组织结构。我们证明，通过将提示混合输入我们的方法，可以从单焦平面扫描中复原高质量病理图像，表明提示混合在临床应用中的巨大潜力。代码将公开发布于 https://github.com/caijd2000/MoP。
英文摘要：
In digital pathology, acquiring all-in-focus images is essential to high-quality imaging and high-efficient clinical workflow. Traditional scanners achieve this by scanning at multiple focal planes of varying depths and then merging them, which is relatively slow and often struggles with complex tissue defocus. Recent prevailing image restoration technique provides a means to restore high-quality pathology images from scans of single focal planes. However, existing image restoration methods are inadequate, due to intricate defocus patterns in pathology images and their domain-specific semantic complexities. In this work, we devise a two-stage restoration solution cascading a transformer and a diffusion model, to benefit from their powers in preserving image fidelity and perceptual quality, respectively. We particularly propose a novel mixture of prompts for the two-stage solution. Given initial prompt that models defocus in microscopic imaging, we design two prompts that describe the high-level image semantics from pathology foundation model and the fine-grained tissue structures via edge extraction. We demonstrate that, by feeding the prompt mixture to our method, we can restore high-quality pathology images from single-focal-plane scans, implying high potentials of the mixture of prompts to clinical usage. Code will be publicly available at https://github.com/caijd2000/MoP.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
