基于双路径提示反转的无配对多域组织病理学虚拟染色
Unpaired Multi-Domain Histopathology Virtual Staining using Dual Path Prompted Inversion
AI总结：
针对无配对多域病理图像虚拟染色中结构一致性与内容保留的挑战，提出基于双路径提示反转的方法，通过优化视觉提示实现内容-风格解耦，在公开数据集上验证了高结构一致性和准确风格迁移。
摘要：
虚拟染色利用计算机辅助技术将组织化学染色组织样本的风格转移到其他染色类型。在病理图像的虚拟染色中，保持严格的结构一致性至关重要，因为这些图像比自然图像更强调结构完整性。即使轻微的结构改变也可能导致诊断语义信息的偏差。此外，虚拟染色数据的无配对特性可能损害病理诊断内容的保留。为解决这些挑战，我们提出了一种基于提示学习的双路径反转虚拟染色方法，该方法优化视觉提示以控制内容和风格，同时保留完整的病理诊断内容。我们提出的反转技术包含两个关键组成部分：(1) 双路径提示策略，我们利用特征适配器函数生成用于反转的参考图像，为输入图像反转提供风格模板，称为风格目标路径。我们利用输入图像的反转作为结构目标路径，在该路径中采用视觉提示图像以保持结构一致性，同时保留来自风格目标路径的风格信息。在确定性采样过程中，我们通过即插即用的嵌入视觉提示方法实现完整的内容-风格解耦。(2) 染色提示优化，我们仅优化空视觉提示作为双路径反转的“操作符”，而不是微调预训练模型。我们在每个时间步围绕关键噪声优化结构路径和风格轨迹的空视觉提示，确保精确的双路径反转重建。在公开可用的多域无配对染色数据集上的广泛评估证明了高结构一致性和准确的风格迁移结果。
英文摘要：
Virtual staining leverages computer-aided techniques to transfer the style of histochemically stained tissue samples to other staining types. In virtual staining of pathological images, maintaining strict structural consistency is crucial, as these images emphasize structural integrity more than natural images. Even slight structural alterations can lead to deviations in diagnostic semantic information. Furthermore, the unpaired characteristic of virtual staining data may compromise the preservation of pathological diagnostic content. To address these challenges, we propose a dual-path inversion virtual staining method using prompt learning, which optimizes visual prompts to control content and style, while preserving complete pathological diagnostic content. Our proposed inversion technique comprises two key components: (1) Dual Path Prompted Strategy, we utilize a feature adapter function to generate reference images for inversion, providing style templates for input image inversion, called Style Target Path. We utilize the inversion of the input image as the Structural Target path, employing visual prompt images to maintain structural consistency in this path while preserving style information from the style Target path. During the deterministic sampling process, we achieve complete content-style disentanglement through a plug-and-play embedding visual prompt approach. (2) StainPrompt Optimization, where we only optimize the null visual prompt as ``operator'' for dual path inversion, rather than fine-tune pre-trained model. We optimize null visual prompt for structual and style trajectory around pivotal noise on each timestep, ensuring accurate dual-path inversion reconstruction. Extensive evaluations on publicly available multi-domain unpaired staining datasets demonstrate high structural consistency and accurate style transfer results.
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