Histological stains, such as hematoxylin and eosin, tend to fade over time, compromising subsequent analysis accuracy. Traditional methods of restoring stain color in faded samples involve physical re-staining, which is time-consuming and expensive and may damage tissue samples. In addition, digital post-processing techniques, such as color normalization, face limitations when dealing with highly faded slides. To address this, we propose the non-invasive phase-to-color “virtual re-staining” framework. This approach utilizes a trained generative adversarial network with label-free quantitative phase imaging, capturing the intrinsic physiochemical properties of histological samples. It employs multi-channel Fourier ptychographic microscopy to generate pixel-wise paired phase and color images in a high-throughput manner. To streamline data generation, near-infrared illumination is used to mitigate the impact of absorption variations in faded and stained samples, eliminating the need for repetitive data acquisition and potential physical alterations in samples. Our trained network yields comparable or better results to other digitally staining methods, successfully demonstrating the re-staining of approximately decade-old faded slides archived in hospital storage.
