Akademik Veri Yönetim Sistemi
Advanced Intelligent Systems, 2026 (SCI-Expanded, Scopus)
Histopathological analysis traditionally relies on hematoxylin and eosin (H&E) staining. However, comprehensive differential diagnoses often require additional histochemical stains, increasing diagnostic time and costs. To address these limitations, we introduce Xstainer, a novel virtual staining tool powered by advanced deep learning techniques. This system adeptly transforms conventional H&E-stained images into multiple histochemical visualizations, marking a significant advancement in diagnostic histopathology. To validate Xstainer's efficacy, we conducted an evaluation study involving experienced nephropathologists. Using the OmniST dataset—a carefully curated collection of 1,646 whole slide images representing diverse patient samples, including renal transplant samples, liver explants, nonmalignant renal disease, and Helicobacter pylori gastritis and paired with standard stains such as Masson's trichrome, Periodic Acid-Schiff, Jones methenamine silver, and Toluidine blue—our tool underwent intensive clinical evaluation. Our virtually stained slides enabled board-certified, experienced nephropathologists (>10) to achieve diagnostic accuracy on par with, if not superior to, traditional staining techniques. Xstainer consistently outperformed various assessment benchmarks, including patch-level visual Turing test, slide-level staining quality assessment, and showed favorable performance in the Fréchet inception distance comparison, further underscoring its transformative potential. In summary, Xstainer offers a promising solution for rapid and accurate histopathological diagnosis with considerable clinical potential.