Abstract: A method for determining a neural network for correcting optical aberrations includes determining one or more images that are at least partly related to an optical system or the design of an optical system. A neural network is determined on the basis of the determined one or more images in such a way that the determined neural network when applied to an image captured by the optical system outputs an image which has been corrected in relation to one or more optical aberrations.

Abstract: A method for determining a focus position includes recording at least one first image, wherein image data of the at least one recorded first image are dependent on at least one first focus position during the recording of the at least one first image. A second focus position is determined based on an analysis of the at least one recorded first image using a trained model. At least one second image is recorded using the second focus position. The at least one first image and the at least one second image contain items of information which are in a context with a training of the trained model.

Abstract: Embodiments relate to a system (100) comprising one or more processors (110) and one or more storage devices (120). The system (100) is configured to receive biology-related language-based search data (101) and generate a first high-dimensional representation of the biology-related language-based search data (101) by a trained language recognition ma-chine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets and compare the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations (105).

Abstract: A method for improving signal-to-noise of image frames is provided. The method includes estimating a representative velocity of an optical flow in an image frame sequence. The method also includes determining an interpolation factor from the representative velocity of the optical flow. The method also includes employing a trained artificial neural network for generating an expanded image frame sequence. The expanded image frame sequence includes a number of interpolating image frames. Each interpolating image frame interpolates between subsequent image frames of the image frame sequence. The number of interpolating image frames corresponds to the interpolation factor. The method also includes computing a time-dependent combination of image frames from the expanded image frame sequence to generate an output image frame sequence.

Abstract: An embodiment of a method 100 for predicting a future state of a biological system is provided. The method 100 comprises receiving 101a microscope image depicting the biological system at an associated time and receiving 102 metadata corresponding to the microscope image. The method 100 further comprises extracting 103 features from the microscope image having information on a state of the biological system and using 104 the features and the metadata to predict the future state of the biological system.

Abstract: An apparatus for scaling images includes one or more processors and one or more computer-readable storage media on which computer-executable instructions are stored. The computer-executable instructions, upon being executed by the one or more processors, provide for execution of the following steps: capturing one or more first images by an imaging and/or image recording system, wherein the one or more captured first images are related to a first resolution; and generating, by a neural network, one or more corresponding second images based on one or more captured first images, wherein the one or more second images are related to a second resolution, the first resolution differing from the second resolution.

Abstract: A system (100) comprising one or more processors (110) and one or more storage devices (120) is configured to obtain biology-related image-based input data (107) and generate a high-dimensional representation of the biology-related image-based input data (107) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to at least one of store the high-dimensional representation of the biology-related image-based input data (107) together with the biology-related image-based input data (107) by the one or more storage devices (120) or output biology-related language-based output data (109) corresponding to the high-dimensional representation.

Abstract: A system (100) comprises one or more processors (110) and one or more storage devices (120), wherein the system (100) is configured to generate a first high-dimensional representation of the biology-related language-based input training data (102) by a language recognition machine-learning algorithm executed by the one or more processors (110). Further, the system (100) is configured to generate biology-related language-based output training data based on the first high-dimensional representation by the language recognition machine-learning algorithm and adjust the language recognition machine-learning algorithm based on a comparison of the biology-related language-based input training data (102) and the biolo-gy-related language-based output training data. Additionally.

Abstract: A system (100) for processing biology-related data comprises one or more processors (110) coupled to one or more storage devices (120). The system (100) is configured to receive biology-related image-based search data (103) and configured to generate a first high-dimensional representation of the biology-related image-based search data (103) by a trained visual recognition machine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system (100) is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets.

Abstract: Embodiments relate to a system (100) comprising one or more processors (110) and one or more storage devices (120). The system (100) is configured to receive biology-related language-based search data (101) and generate a first high-dimensional representation of the biology-related language-based search data (101) by a trained language recognition ma-chine-learning algorithm executed by the one or more processors (110). The first high-dimensional representation comprises at least 3 entries each having a different value. Further, the system is configured to obtain a plurality of second high-dimensional representations (105) of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets and compare the first high-dimensional representation with each second high-dimensional representation of the plurality of second high-dimensional representations (105).

Abstract: A method for determining a neural network for correcting optical aberrations includes determining one or more images that are at least partly related to an optical system or the design of an optical system. A neural network is determined on the basis of the determined one or more images in such a way that the determined neural network when applied to an image captured by the optical system outputs an image which has been corrected in relation to one or more optical aberrations.

Abstract: A method for determining a focus position includes recording at least one first image, wherein image data of the at least one recorded first image are dependent on at least one first focus position during the recording of the at least one first image. A second focus position is determined based on an analysis of the at least one recorded first image using a trained model. At least one second image is recorded using the second focus position. The at least one first image and the at least one second image contain items of information which are in a context with a training of the trained model.

Abstract: A method for optimizing a workflow of at least one microscope or microscope system includes a step a) of implementing a workflow by one or more components of at least one microscope and/or microscope system, wherein the workflow comprises a capture of first data. In a step b), a trained model is determined for the workflow, at least in part based on the captured first data.

Abstract: An apparatus for optimizing workflows of one or more microscopes and/or microscope systems includes one or more processors and one or more computer-readable storage media. The one or more computer-readable storage media have stored therein computer-executable instructions, which, when executed by the one or more processors cause execution of the following steps: implementing, by one or more components of the one or more microscopes and/or microscope systems, a workflow comprising a capture of first data; applying one or more trained models to the captured first data; and making at least one decision in relation to the workflow based on the application of the one or more trained models to the captured first data.