Multi-V-Stain: Multiplexed Virtual Staining of Histopathology Whole-Slide Images

Published: 27 Oct 2023, Last Modified: 11 Nov 2023DGM4H NeurIPS 2023 PosterEveryoneRevisionsBibTeX
Keywords: Generative Adversarial Networks, Computational Pathology, Image-to-Image Translation, Imaging Mass Cytometry (IMC), Protein Expression, Whole-Slide Image
Abstract: Pathological assessment on Hematoxylin \& Eosin (H\&E) stained tissue samples is a clinically-established routine for cancer diagnosis. While providing rich morphological information, it lacks insights on protein expression patterns, essential for cancer prognosis and treatment decisions. Imaging Mass Cytometry (IMC) is adept at highly multiplexed protein profiling. However, it has challenges such as high operational cost and a restrictive focus on small Regions-of-Interest. To this end, we propose Multi-V-Stain, a novel image-to-image translation method for multiplexed IMC virtual staining. Our method can effectively leverage the rich morphological features from H\&E images to predict multiplexed protein expressions on a Whole-Slide Image level. In our assessments using an in-house melanoma dataset, Multi-V-Stain consistently achieves higher image quality and generates stains that are more biologically relevant when compared to existing techniques.
Submission Number: 61