ViFIT-assisted Histopathology: From H&E Style Standardization to Virtual Fiber Image Transformation

Abstract

Deep learning-based virtual fiber staining provides a promising complement to routine H&E pathology. However, the reliance on predefined staining style inputs and manual intervention limits the clinical applicability of existing methods. To address these challenges, we introduce ViFIT-assisted histopathology, a two-stage diagnostic approach that integrates our proposed unsupervised deep learning-based virtual fiber transformation model (ViFIT). This approach enables the conversion of H&E-stained images with diverse styles into pathologist-preferred H&E images, while simultaneously generating content-consistent virtual fiber images containing label-free collagen fibers and stained reticular and elastic fibers. ViFIT-assisted histopathology reveals tumor-associated fibers and provides quantitative metrics across multiple intraoperative and postoperative cases. Experimental results demonstrate that ViFIT significantly outperforms state-of-the-art unsupervised methods in both style standardization and virtual staining, across various downstream tasks and cancer types. By eliminating the need for staining variation and manual annotation, ViFIT-assisted histopathology streamlines histopathology workflows, making it well-suited for multi-center consultations and differential diagnosis.