Siemens Aktiengesellschaft

Other

Biomensio Ltd

Siemens Healthineers Ag

Siemens Healthcare Gmbh

Alexander Michael Gigler

Thomas Engel

Evamaria Stütz

Dorin Comaniciu

Terrence Chen

Ali Kamen

Tommaso Mansi

Shanhui Sun

Matthias Schreiter

Stephan Buchholz

Remigiusz Pastusiak

Patra Charalampaki

Maximilian Fleischer

Manfred Baldauf

Elfriede Simon

Clemens Otte

Matthias Goldammer

Holger Hackstein

Kerstin Wiesner

Stefan Dausend

Arno Arzberger

Sascha Schulte

Frank Steinbacher

Maximillian Fleischer

Stefanie Vogl

Tobias Paust

Daniel Thürauf

Philipp KRÄMER

Lucia Margarita Prieto De Valle

Various embodiments include a method for analyzing an electrode layer for a battery storage device in an electrode layer production facility. The method may include: acquiring an image comprising two pixels of the electrode layer, wherein a first pixel represents a first location of the electrode layer and a second pixel represents a second location of the electrode layer, wherein the first location and the second location are disposed adjacent to each other; determining a first value for a material property of the electrode layer at the first location based on the first pixel; determining a second value for the material property of the electrode layer at the second location based on the second pixel; comparing the first value and the second value and determining a comparison value; and determining characteristic properties of the electrode layer based on the comparison value.

Method for analyzing an electrode layer of a battery storage device, method for producing a battery storage device, and production unit

The invention relates to a method for determining a tissue type of a tissue of an animal or human individual, in which method: electromagnetic radiation () emitted by a tissue sample () of the tissue is sensed () by means of a radiation sensor (), the radiation sensor () providing a sensor signal () in accordance with the sensed electromagnetic radiation, and the sensor signal () is evaluated () by means of an evaluation unit () in order to determine and output the tissue type. The problem addressed by the invention is that of enabling improved determination of the tissue type. According to the invention, the evaluation unit () is a self-learning evaluation unit () that is initially trained () by means of training data sets () on the basis of at least one model, which is based on a method for machine learning, the training of the evaluation unit being conducted by means of such training data sets () each comprising a training sensor signal with an associated training tissue type.

Determining a tissue type of a tissue of an animal or human individual

A test kit that is configured for bioanalysis, such as an immunoassay, is provided. The test kit includes at least one measuring sensor for quantitative detection of a substance, and at least one reference sensor that is already supplied with the substance in a defined manner. In a method for analyzing a test kit, the at least one measuring sensor is read, and a measurement value for a concentration, a substance quantity, or a mass is obtained. A read value of the at least one measuring sensor is scaled using read values of the at least one reference sensor, or a measured value that corresponds to the read value is obtained using a compensation curve that puts the read values of the at least one reference sensor into relationship with the defined supply of the substance to the at least one reference sensor.

Bioanalysis test kit and method for analyzing such a test kit

A method for the computer-assisted configuration of a data-driven model on the basis of training data is provided. The method is characterised in that the series of measurements are subjected to a suitable preprocessing process comprising a binning step, wherein measurement characteristics which existed during the measurement of the measurement values in question are taken into consideration. A suitable data-driven model such as a neural network is then learned on the basis of the pre-processed series of measurements. This learned data-driven model makes it possible to accurately forecast target vectors in accordance with associated series of measurements. The method can, for example, be used to analyse optical spectra. More particularly, it is possible to predict using the learned model whether the tissue sample for which an optical spectrum was detected represents diseased tissue.

Method for the computer-aided configuration of a data-driven model on the basis of training data

Various embodiments include a method for processing a data set comprising: obtaining a measurement dataset; applying a preprocessing algorithm to the measurement dataset to obtain a preprocessed measurement dataset; applying a classification algorithm to the preprocessed measurement dataset to classify a feature represented by the measurement dataset; determining a quality of the classification of the feature; and adjusting the preprocessing algorithm based on the determined quality. Adjusting the preprocessing algorithm comprises applying a selection algorithm. The selection algorithm describes the change in a value of a parameter of the preprocessing algorithm depending on the determined quality. The preprocessing algorithm comprises at least one of the following operations applied to the measurement dataset: binning; differentiation; integration; and forming tuples having a predetermined relationship from the measurement dataset.

Preprocessing for a classification algorithm

The invention relates to a test kit which is designed for bioanalysis, in particular for an immunoassay. The test kit comprises at least one measuring sensor (M) for the quantitative detection of a substance and at least one reference sensor (R, RR) which is already supplied with the substance in a defined manner. In the method for analyzing a test kit, the measuring sensor (M) is read and a measurement value for a concentration, a substance quantity, or a mass is obtained, wherein the read value of the at least one measuring sensor (M) is scaled using the read values of the at least one reference sensor (R, R, R), or a measured value which corresponds to the read value is obtained by means of a compensation curve which puts the read values of the reference sensors (R, R, R) into relationship with the defined supply of the substance to the reference sensors (R, R, R).

Systems and methods for image classification include receiving imaging data of in-vivo or excised tissue of a patient during a surgical procedure. Local image features are extracted from the imaging data. A vocabulary histogram for the imaging data is computed based on the extracted local image features. A classification of the in-vivo or excised tissue of the patient in the imaging data is determined based on the vocabulary histogram using a trained classifier, which is trained based on a set of sample images with confirmed tissue types.

System and method for surgical guidance and intra-operative pathology through endo-microscopic tissue differentiation

An optical sensor is arranged in an indentation of a dust line, the indentation being equipped with at least one gas inlet nozzle for removing the dust from the optical sensor. Dust is transported through the dust line. An optical property of the dust is measured using at least one optical sensor arranged in an indentation of the dust line, and the dust is then removed from the optical sensor by blowing in air using the at least one gas inlet nozzle arranged in the indentation.

Dust line with optical sensor, and method for measuring the composition of dust

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Untermeitingen, Germany

Alexander Michael Gigler