Abstract: The present disclosure relates to the technical field of generation of synthetic images, in particular synthetic medical images. The subjects of the present disclosure are a method, a computer system and a computer-readable storage medium comprising a computer program for detecting artifacts in synthetic images, in particular synthetic medical images.

Abstract: A computing system (122) includes a memory (130) with instructions (132) including a digitally reconstructed radiograph view optimization instruction (134), a processor (128) configured to execute the digitally reconstructed radiograph view optimization instruction to generate a plurality of digitally reconstructed radiographs based on a plurality of different sets of projection parameters and three-dimensional computed tomography image data and to identify an optimal sub-set of the plurality of digitally reconstructed radiographs for reading for the reason for acquiring the three-dimensional computed tomography image data, and an output device (126) configured to display the identified optimal sub-set of the plurality of digitally reconstructed radiographs for reading.

Abstract: A mechanism for providing additional information about a medical image analysis process. A neural network is used to process a medical image, and non-image data elements, to generate an output. An influence of different regions of the medical image and of the non-image data elements to an output of the neural network or intermediate layer output (of the neural network) is determined.

Abstract: The present disclosure is concerned with the technical field of generation of artificial contrast-enhanced radiological images.

Abstract: The present disclosure relates to the technical field of generation of synthetic medical images. The subjects of the present disclosure are a method, a computer system and a computer-readable storage medium comprising a computer program for detecting artifacts in synthetic medical images.

Abstract: The following disclosure relates to the field of data analysis, in particular medical data analysis, or more particularly relates to systems, apparatuses, and methods for processing in particular medical data stored in different modalities, so-called multi-modal data. In some embodiments, the disclosure relates to similarity retrieval for input data, in particular medical input data.

Abstract: A system (SYS) and related method for providing training data. The system is configured to receive a classification result for a class from plural pre-defined classes (Cj). The classification result is produced by a trained machine learning model (M) in response to processing an input image. A decision logic (DL) of the system is configured to analyze input data (pi,qt) comprising the received classification result value (pi) and an uncertainty value (qi) associated with the classification result value. The system outputs, per class, an associated indication whether the input image is or is not useful for re-training the model (M) in respect of the said class.

Abstract: Systems, methods, and computer programs disclosed herein relate to training and using a machine learning model to detect, identify and/or characterize body actions, in particular cough events.

Abstract: The present disclosure relates to the early detection of the presence or occurrence of a condition in an object of investigation and/or the occurrence of an event in an object of investigation by means of machine learning methods. The subject matter of this disclosure consists of a computer-implemented method, a computer system, and a computer program for the early detection of such conditions and/or events.

Abstract: The invention provides for a method for switching between fields of view of an ultrasound probe. The method begins by obtaining an anatomical model representing a region of interest of a subject and establishing a first field of view relative to an ultrasonic probe, wherein the first field of view comprises an initial portion of the region of interest. Ultrasound data is then obtained from the first field of view by way of the ultrasonic probe and a first anatomical feature is identified within the first field of view based on the ultrasound data. A location in digital space of the first field of view relative to the anatomical model is determined based on the first anatomical feature. A second field of view is then established based on the anatomical model and the first field of view, wherein the first field of view functions as a reference field of view. The field of view is then switched from the first field of view to the second field of view.

Abstract: The present invention relates to the technical field of federated learning. Subject matter of the present invention is a method for (re-)training a federated learning system, a computer system for carrying out the method, and a non-transitory computer-readable storage medium comprising processor-executable instructions with which to perform an operation for (re-)training a federated learning system.

Abstract: The present invention relates to the technical field of producing artificial contrast-enhanced radiological images by way of machine learning methods.

Abstract: A method for training a machine learning model that is able to establish links between data of different modalities by creating a joint representation. In particular, application of the method to medical data including electronic medical records and medical images and/or other medical data. The trained machine learning model can among others fulfil tasks such as autocompletion of incomplete data, detection of uncertain and/or spurious data, generation of probable data and other tasks.

Abstract: The present invention relates to the technical field of machine learning. Subject matter of the present invention is a novel approach for training a neural network and the use of this approach for the processing of (medical) images.

Abstract: The present invention relates to the technical field of producing artificial contrast-enhanced radiological images by way of machine learning methods.

Abstract: A method and system for correcting a difference in contrast agent density in a sequence of contrast-enhanced image frames. A reference image frame is defined in the sequence of contrast-enhanced image frames, and segmentation is performed on the reference image frame to determine a location of a region of interest within the reference image frame. The region of interest is a region of the reference image frame that contains contrast agent. Other image frames in the sequence of contrast-enhanced images are corrected based on a difference in contrast agent density/image intensity in the region of interest relative to the region of interest in the reference image frame.

Abstract: Systems, methods, and computer programs disclosed herein relate to training a machine learning model to generate multimodal representations of objects, and to the use of said representations for predictive purposes.

Abstract: A method for providing a prediction of a representation of an examination region that was generated using a medical image technique involving a contrast agent may include receiving a first representation in frequency space of an examination region of an examination object, receiving a second representation in the frequency space of the examination region of the examination object, providing the first representation and the second representation as an input to a predictive machine learning model that is configured to provide, as an output, a prediction of a representation in the frequency space of the examination region with an amount of the contrast agent administered during a medical imaging technique, receiving the output of the predictive machine learning model based on the input, and converting the output of the predictive machine learning model to a representation in real space of the examination region of the examination object.

Abstract: The present invention relates to the technical field of producing artificial contrast-enhanced radiological images by way of machine learning methods.

Abstract: A system and method for training a machine learning module to provide classification and localization information for an image study. The method includes receiving a current image study. The method includes applying the machine learning module to the current image study to generate a classification result including a prediction for one or more class labels for the current image study using User Interface 104 a classification module of the machine learning module. The method includes receiving, via a user interface, a user input indicating a spatial location corresponding to a predicted class label. The method includes training a localization module of the machine learning module using the user input indicating the spatial location corresponding to the predicted class label.