@inproceedings{
andani2023multivstain,
title={Multi-V-Stain: Multiplexed Virtual Staining of Histopathology Whole-Slide Images},
author={Sonali Andani and Boqi Chen and Joanna Ficek-Pascual and Simon Heinke and Ruben Casanova and Bettina Sobottka and Bernd Bodenmiller and Viktor Koelzer and Gunnar Ratsch},
booktitle={Deep Generative Models for Health Workshop NeurIPS 2023},
year={2023},
url={https://openreview.net/forum?id=3tu9EnRoWl}
}
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.
Multi-V-Stain: Multiplexed Virtual Staining of Histopathology Whole-Slide Images
Sonali Andani Boqi Chen Joanna Ficek-Pascual Simon Heinke Ruben Casanova Bettina Sobottka Bernd Bodenmiller Viktor Koelzer Gunnar Ratsch