A computer-implemented method for producing a virtually stained digital holographic microscopy image, comprising: receiving a DHM image acquired using a DHM device, the DHM image comprising depictions of one or more cells; and virtually staining, by a virtual staining device the DHM image by processing the DHM image using an image conversion algorithm, wherein the virtually stained DHM image includes digital colorization of the one or more cells to imitate the appearance of a corresponding actually stained cell, characterized in that, the image conversion algorithm is being trained using generative adversarial networks, receiving, at a training device, a first training data set comprising DHM images of individual white blood cells and a second training data set comprising images of actually stained white blood cells, wherein the images in the first training data set are not paired with the images in the second training data set; and applying, by the training device, a learning process which uses the generative adversarial networks to the first training data set and the second training data set to generate the image conversion algorithm, wherein the generative adversarial networks comprise a first generative network configured to generate a plurality of virtually stained DHM images and a first discriminating network configured to distinguish between the plurality of virtually stained DHM images and images in the second training data set, wherein the generative adversarial networks further comprise a second generative network configured to generate a plurality of virtual DHM images and a second discriminating network configured to distinguish between the plurality of virtual DHM images and images in the first training data set. receiving a DHM image acquired using a DHM device, the DHM image comprising depictions of one or more cells; and virtually staining, by a virtual staining device the DHM image by processing the DHM image using an image conversion algorithm, wherein the virtually stained DHM image includes digital colorization of the one or more cells to imitate the appearance of a corresponding actually stained cell, characterized in that, the image conversion algorithm is being trained using generative adversarial networks, receiving, at a training device, a first training data set comprising DHM images of individual white blood cells and a second training data set comprising images of actually stained white blood cells, wherein the images in the first training data set are not paired with the images in the second training data set; and applying, by the training device, a learning process which uses the generative adversarial networks to the first training data set and the second training data set to generate the image conversion algorithm, wherein the generative adversarial networks comprise a first generative network configured to generate a plurality of virtually stained DHM images and a first discriminating network configured to distinguish between the plurality of virtually stained DHM images and images in the second training data set, wherein the generative adversarial networks further comprise a second generative network configured to generate a plurality of virtual DHM images and a second discriminating network configured to distinguish between the plurality of virtual DHM images and images in the first training data set.