One-side Virtual Histological Staining Model for Complex Human Samples

Deep learning-assisted low-cost autofluorescence microscopy for rapid slide-free imaging with virtual histological staining

“…5(a), (b) ). Due to the sample complexity of human cancer tissue, we have further optimized the virtual staining performance based on an unsupervised one-sided algorithm [ 24 ]. In Fig.…”

“…Furthermore, we also found challenges in virtual staining on more complex human samples. Due to the feature complexity of human cancer tissue, we employed an unsupervised one-sided algorithm for virtual staining [ 24 ], which incorporated a region classification loss to improve the classification ability of the discriminator. While more and better data should help improve the virtual staining performance, the future direction could consider incorporating certain degrees of labeling other than pixel-wise alignment, e.g., by introducing classes for better feature learning.…”

“…Because of the complex features of the human cancer tissues, we employed an unsupervised one-sided algorithm for virtual staining on the human lung tissue to optimize the virtual staining performance [ 24 ]. The training dataset was composed of randomly sampled 5,000 image patches with a size of 256 × 256.…”

“…The unsupervised one-sided algorithm used in virtual staining deviates from the cycleGAN-like two-sided framework and only requires a single generator and discriminator [ 24 ]. The generator used in this part is a U-shape neural network with shortcut connections [ 27 ], while the discriminator is inspired by the one proposed in pix2pixHD [ 28 ], a multiple-branch discriminator for better feature representation.…”