Siemens Healthcare Diagnostics Gmbh

Siemens Healthcare Gmbh

Daniela Seidel

Gaby Marquardt

Christoph Tietz

Ali Kamen

Oliver Schmidt

Thomas Engel

Markus Michael Geipel

Florian Büttner

Manfred Stanzel

Oliver Hayden

Noha Youssry El-Zehiry

Lukas Richter

Matthias Ugele

Stefan Depeweg

Gabriele Hörnig

To train a machine learning routine (BNN), a sequence of first training data (PIC) is read in through the machine learning routine. The machine learning routine is trained using the first training data, wherein a plurality of learning parameters (LP) of the machine learning routine is set by the training. Furthermore, a value distribution (VLP) of the learning parameters, which occurs during the training, is determined and a continuation signal (CN) is generated on the basis of the determined value distribution of the learning parameters. Depending on the continuation signal, the training is then continued with a further sequence of the first training data or other training data (PIC) are requested for the training.

Method and device for training a machine learning routine for controlling a technical system

An image analysis device (BA) is configured to recognize imaged objects. A plurality of training images (TPIC) assigned to an object type (OT) and an object sub-type (OST) are fed into a first neural network module (CNN) to detect image features. Training output data sets (FEA) of the first neural network module are fed into a second neural network module (MLP) to detect object types using the detected image features. For each object type: training images assigned to the object type (OT, OT) are fed into the trained first neural network module, the first neural network module training output data set (FEA, FEA) generated for the respective training image is assigned to the object sub-type (OST) of the respective training image, and by means of the aforementioned sub-type assignments, a sub-type detection module (BMLP, BMLP) is configured to detect object sub-types.

Method for configuring an image evaluation device and also image evaluation method and image evaluation device

A computer-implemented method and a data processing apparatus provide and apply a trained probabilistic graphical model for verifying and/or improving the consistency of labels within the scope of medical image processing. Also provided are a computer-implemented method for verifying and/or improving the consistency of labels within the scope of medical imaging processing, a data processing apparatus embodied to verify and/or improve the consistency of labels within the scope of medical image processing, and a corresponding computer program product and a computer-readable medium.

Verifying and/or improving the consistency of labels within the scope of medical image processing

The present invention relates to an improved method for marker-free detection of a cell type of at least one cell in a medium using microfluidics and digital holographic microscopy, as well as a device, particular for carrying out the method.

High accuracy 5-part differential with digital holographic microscopy and untouched leukocytes from peripheral blood

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Baiersdorf, Germany

Daniela Seidel