The effect of neural network architecture on virtual H&E staining: Systematic assessment of histological feasibility.

ABSTRACT: Conventional histopathology has relied on chemical staining for over a century. The staining process makes tissue sections visible to the human eye through a tedious and labor-intensive procedure that alters the tissue irreversibly, preventing repeated use of the sample. Deep learning-based virtual staining can potentially alleviate these shortcomings. Here, we used standard brightfield microscopy on unstained tissue sections and studied the impact of increased network capacity on the resulting virtually stained H&E images. Using the generative adversarial neural network model pix2pix as a baseline, we observed that replacing simple convolutions with dense convolution units increased the structural similarity score, peak signal-to-noise ratio, and nuclei reproduction accuracy. We also demo

SUBMITTER: Khan U 

PROVIDER: S-EPMC10201298 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature