Abstract: A method of virtual staining of a tissue sample includes obtaining imaging data depicting the tissue sample. The method also includes processing the imaging data in at least one machine-learning logic, the at least one machine-learning logic being configured to provide multiple output images all comprising a given virtual stain of the tissue sample, the multiple output images depicting the tissue sample comprising the given virtual stain at different colorings associated with different staining laboratory processes. The method further includes obtaining, from the at least one machine-learning logic, at least one output image of the multiple output images.

Abstract: A method, a device, and a computer program product captures microscopy objects in image data that includes first images recorded with a first contrast and second images recorded with a second contrast, wherein in each case, one of the first and one of the second images can be correspondingly assigned to each other. The method includes capturing information indicating microscopy objects in at least one of the second images, transferring the captured information to those of the first images which correspond to the at least one of the second images, and capturing information indicating microscopy objects in the first images, to which the captured information of the second images was transferred by using the transferred information.

Abstract: In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.

Abstract: In a computer-implemented method for generating an image processing model that generates output data defining a stylized contrast image from a microscope image, model parameters of the image processing model are adjusted by optimizing at least one objective function using training data. The training data comprises microscope images as input data and contrast images, wherein the microscope images and the contrast images are generated by different microscopy techniques. In order for the output data to define a stylized contrast image, the objective function forces a detail reduction or the contrast images are detail-reduced contrast images with a level of detail that is lower than in the microscope images and higher than in binary images.