Abstract: A computer-implemented method, comprising: receiving input data including a medical image and an in-image annotation; applying a first function to the input data to determine a relevance value of pixels in the image and a relevance map; applying a second function to the medical image to generate a de-identified medical image; applying a trained function to the medical image and the de-identified medical image to determine a first property in the medical image and a second property in the de-identified medical image; applying a comparison function to the first property and the second property to determine a similarity value, wherein in response to the similarity value being below a similarity threshold, the relevance map is adjusted and the applying of the second function, the applying of the trained function and the applying of the comparison function are repeated; and providing the de-identified medical image and the in-image annotation.

Abstract: A computer-implemented method comprises: receiving input data, wherein the input data includes the X-ray image dataset, which includes an X-ray image and first metadata; applying a trained function to the input data to generate output data, wherein the output data includes second metadata, and wherein the first metadata and the second metadata are compared; and providing the output data, wherein the first metadata are confirmed in case the first metadata and the second metadata agree, or the first metadata are suggested to be corrected with the second metadata in case the first metadata and the second metadata do not agree.

Abstract: A method is for determining a patient-adjusted breast compression in mammography. In an embodiment of the method, input data including individual, person-related data of a female patient, is determined. Furthermore, an adjusted individual compression point is determined by applying a function, trained by an algorithm based on machine learning, to the input data. The adjusted individual compression point is generated as the output data. Other embodiments include a method for providing a trained function; a breast compression determining device; a training device; and a mammography system.

Abstract: A computer program, a system and a method for normalizing medical images from a type of image acquisition device using a machine learning unit are disclosed. An embodiment of the method includes receiving a set of image data with images; decomposing each of the images of the set of images into components by incorporating at least information from different settings of the image acquisition device-specific image processing algorithms; and normalizing each of the components via a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images with a relatively decreased variability score.

Abstract: One or more example embodiments relates to a computer-implemented method for determining an abnormal structure in an examination region in conjunction with an X-ray recording of an X-ray system, comprising receiving input data, the input data relating to an X-ray recording data set of the X-ray recording having multiple data channels; applying a trained function to the input data, the trained function being based on a machine learning method and applied to at least two data channels to determine the abnormal structure and generate output data; and providing the output data, the output data including an abnormal structure of the examination region. 5407544.

Abstract: A method for automatically analysing 2D medical image data, including an additional object comprises acquiring the 2D medical image data from an examination portion of a patient using a first modality; acquiring additional image data from the examination portion using a different modality; and performing an automatic image analysis based on the acquired 2D medical image data and the acquired additional image data, the image analysis being adapted to the additional object.

Abstract: According to a method for correcting a 2D measurement value is described, 2D image data of an examination object is received. Landmarks in the 2D image data are detected, and 2D positions of the landmarks are calculated. A corrected measurement value of the examination object is predicted, using a trained model, which depends on the received 2D image data, the estimated 2D positions of the landmarks and a reference parameter of a reference 3D orientation of the examination object.

Abstract: In a method, comparison features are extracted from labeled reference image data. Features are also extracted from the image data. A statistical comparison of the comparison features with the features then takes place. On the basis of the statistical comparison and a quality criterion, the quality of the AI-based result data is determined. A method for correcting result data is additionally described. Furthermore, a method for AI-based acquisition of result data on the basis of measured examination data is described. Also described is a validation entity. An entity for correcting result data is additionally described. Furthermore, an entity for acquiring result data is described. Also described is a medical imaging entity.

Abstract: A method for evaluation of medical image data comprises: providing medical image data of a patient to be examined; determining, for at least one segment of the medical image data, a respective classification probability value with respect to at least one classification from a list of specified classifications; determining a patient-specific relevance criterion for at least one classification for at least the at least one segment of the medical image data; and determining a clinical relevance of the at least one classification for the at least one segment of the medical image data using the patient-specific relevance criterion, and at least one of based on the classification probability values or based on the at least one segment of the medical image data.

Abstract: A computer-implemented method is for providing at least one first metadata attribute associated with a medical image. The method includes receiving the medical image and the at least one first metadata attribute. Therein the at least one first metadata attribute includes an attribute tag and a provisional attribute value. Furthermore, the method includes applying a first trained function to the medical image to determine an image-based attribute value. Furthermore, the method includes determining a final attribute value based on the provisional attribute value and the image-based attribute value. Furthermore, the method includes providing the at least one first metadata attribute. Therein the at least one first metadata attribute includes the attribute tag and the final attribute value.

Abstract: A method is for determining a positioning quality of an external apparatus inserted into a patient's body. In the method, image data of the patient's body is detected; an at least one first anatomical landmark is detected in or with a first subregion, in which the external apparatus is positioned as expected; at least a first subsection of the external apparatus is sought in or with the at least one first subregion; a second anatomical landmark is detected in or with at least one second subregion; at least one second subsection is then detected based upon the already localized subsection; and a quality of the positioning of the at least one second subsection is determined by measuring a suitable dimension between the localized at least one second subsection and the at least one second landmark. A training method, a positioning quality determination facility and a training facility are also disclosed.

Abstract: A method is for determining a patient-adjusted breast compression in mammography. In an embodiment of the method, input data including individual, person-related data of a female patient, is determined. Furthermore, an adjusted individual compression point is determined by applying a function, trained by an algorithm based on machine learning, to the input data. The adjusted individual compression point is generated as the output data. Other embodiments include a method for providing a trained function; a breast compression determining device; a training device; and a mammography system.

Abstract: A computer program, a system and a method for normalizing medical images from a type of image acquisition device using a machine learning unit are disclosed. An embodiment of the method includes receiving a set of image data with images; decomposing each of the images of the set of images into components by incorporating at least information from different settings of the image acquisition device-specific image processing algorithms; and normalizing each of the components via a machine learning unit by processing at least information from the different settings of the image acquisition device-specific processing algorithms to provide a set of normalized images with a relatively decreased variability score.