The above techniques can be combined with computational methods to estimate staining levels without staining the cell. These approaches, generally, rely on training a deep-convolutional neural network to perform imaging remapping, converting the bright-field or phase image into a fluorescent image. By decoupling the training corpus from the cells under investigation, these approaches provide an avenue for using stains that are otherwise incompatible with live cell imaging, such as anti-body staining.