Abstract: A prediction method to predict a value of a target variable in an input dataset includes analyzing a plurality of instances of the input dataset to predict a plurality of values of the target variable. A supervised machine learning model is trained on the plurality of instances and respective predicted plurality of values, and the trained supervised machine learning model is thereafter used to predict the value of a target variable in the input dataset.

Abstract: A system for monitoring a data-driven model, configured to perform a task in a plurality of sites, includes a plurality of variants of the data-driven model deployed in each site. Each variant is used in one of a plurality of states including a first state wherein the output data of the variant is included in computing a result of the task, and a second state wherein the output data of the variant is excluded from computing the result of the task. A supervision module in each site monitors the plurality of variants, computes the task result based on the output data generated by each variant being used in the first state, and changes, based on the output data generated by variants being used in the first state and in the second state, the use state of a variant from one state to another.

Abstract: A system for monitoring a data-driven model, configured to perform a task in a plurality of sites, includes a plurality of variants of the data-driven model deployed in each site. Each variant is used in one of a plurality of states including a first state wherein the output data of the variant is included in computing a result of the task, and a second state wherein the output data of the variant is excluded from computing the result of the task. A supervision module in each site monitors the plurality of variants, computes the task result based on the output data generated by each variant being used in the first state, and changes, based on the output data generated by variants being used in the first state and in the second state, the use state of a variant from one state to another.

Abstract: A machine learning method for medical image diagnostic tasks includes (i) iteratively performing an unsupervised learning step of a plurality of unsupervised learning models from a set of unlabeled patient data to extract features from said unlabeled patient data, and (ii) performing on an ad hoc basis a supervised learning step using extracted features to learn a plurality of supervised learning models from a first set of labeled medical images for a first medical image diagnostic task, and from a second set of labeled medical images for a second medical image diagnostic task different from the first medical image diagnostic task.

Abstract: A system to enable application of a function or service to a medical image when displayed by a medical image viewer, including: a tagging unit configured to apply a visual tag on the medical image so that the visual tag is visible when a tagged medical image is displayed by the medical image viewer, the visual tag containing information relating to the function or service; and an application unit configured to identify the applied visual tag when the tagged medical image is displayed by the medical image viewer, the application unit being further able to apply the function or service to the medical image based on information contained in the identified visual tag.

Abstract: A computing device for processing image data in a medical evaluation workflow is described. The computing device receives image data of a patient and metadata associated with the image data. The computing device analyzes pixel information of the image data using a particular machine learning algorithm to determine one or more acquisition conditions. The computing device selects a machine learning algorithm from a set of machine learning algorithms based on the determined one or more acquisition conditions. The particular machine learning algorithm is different from each machine learning algorithm of the set of machine learning algorithms. The computing device analyzes the pixel information of the image data and the metadata using the selected machine learning algorithm to determine one or more medical conditions of the patient.