Leica Microsystems Cms Gmbh

Constantin Kappel

Frank Sieckmann

Florian Fahrbach

Kai Walter

Luciano Andre Guerreiro-lucas

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.

Optimization of workflows for microscopes

A system () comprising one or more processors () and one or more storage devices () is configured to obtain biology-related image-based input data () and generate a high-dimensional representation of the biology-related image-based input data () by a trained visual recognition machine-learning algorithm executed by the one or more processors (). 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 () together with the biology-related image-based input data () by the one or more storage devices () or output biology-related language-based output data () corresponding to the high-dimensional representation.

System and method for processing biology-related data and a microscope

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.

Changing the size of images by means of a neural network

A system () for processing biology-related data comprises one or more processors () coupled to one or more storage devices (). The system () is configured to receive biology-related image-based search data () and configured to generate a first high-dimensional representation of the biology-related image-based search data () by a trained visual recognition machine-learning algorithm executed by the one or more processors (). 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 () of a plurality of biology-related image-based input data sets or of a plurality of biology-related language-based input data sets. Additionally, the system () is configured to compare the first high-dimensional representation with each second high-dimensional representation () of the plurality of second high-dimensional representations by the one or more processors ().

System and method for processing biology-related data, a system and method for controlling a microscope and a microscope

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.

Inference microscopy

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.

Learning autofocus

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.

Optical correction via machine learning

Embodiments relate to a system () comprising one or more processors () and one or more storage devices (). The system () is configured to receive biology-related language-based search data () and generate a first high-dimensional representation of the biology-related language-based search data () by a trained language recognition ma-chine-learning algorithm executed by the one or more processors (). 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 () 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 ().

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Schriesheim, Germany

Constantin Kappel