Abstract: A computer-implemented method of training a causal generative model for counterfactual medical image generation includes obtaining medical imaging data including images depicting anatomy of patients and associated causal parameters, obtaining a causal generative model with structural causal functions defining relationships between causal parameters, an encoder network, and a decoder network, and iteratively determining latent parameter noise and a latent representation, obtaining spatially targeted interventions, generating counterfactual parameters and a counterfactual image, determining a loss by applying a pre-trained segmentor to obtain segmentation outputs, determining the loss based on the difference between measurements and counterfactual parameters, and updating the causal generative model.

Abstract: Systems and methods for enhancing quality control in medical image segmentation is provided. A medical image may be automatically segmented to obtain a segmentation, or quantifying objects may be detected in the medical image to obtain one or more object detections. A predetermined error may be introduced into the segmentation or one or more object detections. The segmentation or one or more object detections may be provided for output. It may be determined whether the predetermined error was detected in the segmentation or one or more object detections.

Abstract: A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and/or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific pre

Abstract: A computer-implemented method for predicting cardiovascular disease risk, the method including: receiving a first patient history data comprising imaging data and/or non-imaging data for a patient at a first time point; selecting prediction report parameters defining a type of cardiovascular event and a risk prediction time scale; processing the first patient history data using a trained machine learning model configured to predict disease onset, progression, and/or regression over time, wherein the trained machine learning model is trained using patient subsets created based on patient history characteristics and outcomes; generating a risk prediction for the selected type of cardiovascular event over the selected risk prediction time scale based on the processed first patient history data; generating a risk prediction report based on the risk prediction; and outputting the risk prediction report.

Abstract: To fold a piece of linen to a very small final dimension, for example a bed sheet to 9″×6″, the piece of linen is kept constantly under pressure after final folding until it has been fixed in a storage position, in order to prevent opening of said final fold. This can be effected by pushing it during final folding by means of a folding sword into a receptacle, from where it is pushed by means of a ram onto a stacking line where its outer surfaces each rest against a corresponding outer surface of an adjacent piece of linen or another stop surface formed by slides or a pressure plate, so that it is fixed in its final configuration. Alternatively, the piece of linen can be fed, while maintaining pressure on the outer surfaces, to a packing apparatus and surrounded there by a strip of packaging material.

Abstract: To fold a piece of linen (5) to a very small final dimension, for example a bed sheet to 9″×6″, said piece of linen is kept constantly under pressure after final folding until it has been fixed in a storage position, in order to prevent opening of said final fold. This can be effected by pushing it during final folding by means of a folding sword into a receptacle (20), from where it is pushed by means of a ram (26) onto a stacking line (27) where its outer surfaces each rest against a corresponding outer surface of an adjacent piece of linen (5′) or another stop surface formed by slides (31a,b) or a pressure plate (33), so that it is fixed in its final configuration. Alternatively, the piece of linen can be fed, while maintaining pressure on the outer surfaces, to a packing apparatus and surrounded there by a strip of packaging material.

Abstract: To fold a piece of linen (5) to a very small final dimension, for example a bed sheet to 9″×6″, said piece of linen is kept constantly under pressure after final folding until it has been fixed in a storage position, in order to prevent opening of said final fold. This can be effected by pushing it during final folding by means of a folding sword into a receptacle (20), from where it is pushed by means of a ram (26) onto a stacking line (27) where its outer surfaces each rest against a corresponding outer surface of an adjacent piece of linen (5′) or another stop surface formed by slides (31a,b) or a pressure plate (33), so that it is fixed in its final configuration. Alternatively, the piece of linen can be fed, while maintaining pressure on the outer surfaces, to a packing apparatus and surrounded there by a strip of packaging material.