1 citations · 1 across the 4 of their papers we have counts for
4 papers
Building Trust in Virtual Immunohistochemistry: Automated Assessment of Image Quality
Tushar Kataria, Shikha Dubey, Mary Bronner +4
Deep learning models can generate virtual immunohistochemistry (IHC) stains from hematoxylin and eosin (H&E) images, offering a scalable and low-cost alternative to laboratory IHC.…
IMPLICITSTAINER: Resolution Agnostic Data-Efficient Virtual Staining Using Neural Implicit Functions
Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian
Hematoxylin and eosin (H&E)-stained slides are central to cancer diagnosis and monitoring, visualizing tissue architecture and cellular morphology. However, H&E lacks the molecular…
VIMs: Virtual Immunohistochemistry Multiplex staining via Text-to-Stain Diffusion Trained on Uniplex Stains
Shikha Dubey, Yosep Chong, Beatrice Knudsen +1
This paper introduces a Virtual Immunohistochemistry Multiplex staining (VIMs) model designed to generate multiple immunohistochemistry (IHC) stains from a single hematoxylin and e…
StainDiffuser: MultiTask Dual Diffusion Model for Virtual Staining
Tushar Kataria, Beatrice Knudsen, Shireen Y. Elhabian
Hematoxylin and Eosin (H&E) staining is widely regarded as the standard in pathology for diagnosing diseases and tracking tumor recurrence. While H&E staining shows tissue structur…