  Hematoxylin and Eosin (H&E) staining is the most commonly used for disease
diagnosis and tumor recurrence tracking. Hematoxylin excels at highlighting
nuclei, whereas eosin stains the cytoplasm. However, H&E stain lacks details
for differentiating different types of cells relevant to identifying the grade
of the disease or response to specific treatment variations. Pathologists
require special immunohistochemical (IHC) stains that highlight different cell
types. These stains help in accurately identifying different regions of disease
growth and their interactions with the cell's microenvironment. The advent of
deep learning models has made Image-to-Image (I2I) translation a key research
area, reducing the need for expensive physical staining processes. Pix2Pix and
CycleGAN are still the most commonly used methods for virtual staining
applications. However, both suffer from hallucinations or staining
irregularities when H&E stain has less discriminate information about the
underlying cells IHC needs to highlight (e.g.,CD3 lymphocytes). Diffusion
models are currently the state-of-the-art models for image generation and
conditional generation tasks. However, they require extensive and diverse
datasets (millions of samples) to converge, which is less feasible for virtual
staining applications.Inspired by the success of multitask deep learning models
for limited dataset size, we propose StainDiffuser, a novel multitask dual
diffusion architecture for virtual staining that converges under a limited
training budget. StainDiffuser trains two diffusion processes simultaneously:
(a) generation of cell-specific IHC stain from H&E and (b) H&E-based cell
segmentation using coarse segmentation only during training. Our results show
that StainDiffuser produces high-quality results for easier (CK8/18,epithelial
marker) and difficult stains(CD3, Lymphocytes).
