Microscope and method with implementation of a convolutional neural network

US12124021B2 · kind B2 · utility

Assignee

• Carl Zeiss Microscopy GmbH · DE

Inventors

• Manuel Amthor · Jena, DE
• Daniel Haase · Zöllnitz, DE

Key dates

• Filing date: Nov 13, 2020
• Grant date: Oct 22, 2024
• Expiry date: Dec 13, 2041

Classification

• Technology area (CPC G): Physics
• CPC primary: G06N3/082
• WIPO field: Computer technology
• WIPO sector: Electrical engineering

Abstract

A method for processing microscope images in order to generate an image processing result comprises: implementing a convolutional neural network, wherein a first convolutional layer calculates an output tensor from an input tensor formed from a microscope image. The output tensor is input into one or more further layers of the convolutional neural network in order to calculate the image processing result. The first convolutional layer comprises a plurality of filter kernels. At least several of the filter kernels are respectively representable by at least one filter matrix with learning parameters and dependent filter matrices with implicit parameters, which are determined by means of the learning parameters and one or more weights to be learned, wherein the filter matrices with learning parameters of different filter kernels are different from one another and different layers of the output tensor are calculated by different filter kernels.