Unpaired Multi-Domain Histopathology Virtual Staining Using Dual Path Prompted Inversion

作者：Bing Xiong, Peng Yue, Ranran Zhang, Fuqiang Chen, Jiaye He, Wenjian Qin · 发表于：Proceedings of the AAAI Conference on Artificial Intelligence · 年份：2025 · DOI：10.1609/aaai.v39i8.32949 · 被引用次数：4 · 研究领域：AI in cancer detection、Ultrasound Imaging and Elastography、Photoacoustic and Ultrasonic Imaging

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...