Abstract: Heart strain determination includes receiving a series of 2D-slice images as input. A pose estimation module estimates a slicing-pose of the inputted series of 2D-slice images in the heart. A 3D deformation estimation module estimates a 3D deformation field from the series of 2D-slice images and the estimated slicing-pose. A strain measurement module computes a heart strain measure from the 3D deformation field and a predefined definition for strain computation.

Abstract: Systems and methods for performing a medical imaging analysis task from PCCT (photon counting computed tomography) imaging data are provided. PCCT imaging data acquired from a PCCT imaging device is received. A plurality of PCCT virtual images is generated from the PCCT imaging data. A plurality of medical imaging analysis sub-tasks is performed based on the plurality of PCCT virtual images using a plurality of machine learning based networks. Results of the medical imaging analysis sub-tasks are combined to generate results of a medical imaging analysis task. The results of the medical imaging analysis task are output.

Abstract: Systems and methods for training a machine learning based network based on PCCT (photon counting computed tomography) imaging data. PCCT imaging data acquired from a PCCT imaging device is received. One or more PCCT virtual images are generated from the PCCT imaging data. A machine learning based network is trained for performing a medical imaging analysis task based on the one or more PCCT virtual images. The trained machine learning based network is output.

Abstract: Systems and method for safe over-the-air (OTA) update of electronic control units in vehicles are provided. The OTA server determines whether an operator terminal is close to a vehicle and if so, sends a request to the operator terminal requesting confirmation for proceeding with completing a firmware update for an electronic control unit in the vehicle. In response to receiving the confirmation, the OTA server sends a request to a telematics device coupled to the vehicle to complete the firmware update.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: Techniques of training an image-synthesis ML algorithm are disclosed. The image-synthesis ML algorithm can be used to generate synthetic imaging data. The synthetic imaging data can be used, in turn, to train a further ML algorithm. The further ML algorithm may be configured to perform image-processing tasks on the respective imaging data.

Abstract: Systems and methods for determining a semantic image understanding of medical imaging studies are provided. A plurality of medical imaging studies associated with a plurality of medical imaging modalities is provided. Metadata associated with each of the plurality of medical imaging studies is generated by performing a plurality of semantic image analysis tasks using one or more machine learning based networks. The metadata associated with each of the plurality of medical imaging studies is output.

Abstract: A system maintains a queue structure used for storing packets and comprising a plurality of sub-queues used to process the packets, wherein the packets in the queue structure are to be dequeued by a scheduler. The system computes a respective packet virtual time for a respective packet based on at least a packet virtual time of a previous packet processed by the same sub-queue. The system computes a global virtual time based on a packet virtual time of a packet being dequeued from the queue structure. The system measures a rate at which the global virtual time progresses based on the virtual time of packets dequeued from the queue structure. The system manages congestion in the sub-queues based on the rate at which the global virtual time progresses, a metric of a respective sub-queue, and an amount of a resource for the queue structure.

Abstract: A system maintains ordered sub-queues used for storing packets, which are to be dequeued by a scheduler. A respective is enqueued into a sub-queue, and a virtual time associated with the respective packet is based on a current sub-queue virtual time corresponding to a previously enqueued packet in the sub-queue. The system dequeues, by the scheduler, a first packet from a selected sub-queue and determines a packet virtual time associated with a next packet in the currently selected sub-queue. Responsive to determining that the packet virtual time associated with the next packet is greater than a current global virtual time, the system selects a next sub-queue in the ordered plurality of sub-queues. The system updates the current global virtual time based on a packet virtual time of the dequeued first packet.

Abstract: A system maintains a queue for storing packets, which are enqueued at a tail of the queue and dequeued at a head of the queue. The system computes a queue utilization value, based on the packets stored in the queue. The system computes an excess amount value, based on the packets stored in the queue and previously tagged as excess packets. The system receives a first packet at the tail of the queue and determines whether a difference between the queue utilization value and the excess amount value exceeds a predetermined threshold. Responsive to determining that the difference exceeds the predetermined threshold, the system tags the first packet as an excess packet. Responsive to tagging the first packet as an excess packet, the system performs an operation associated with the first packet or a second packet at the head of the queue to reduce congestion.

Abstract: Systems and methods are provided for optimally allocating resources used to perform multiple tasks/jobs, e.g., machine learning training jobs. The possible resource configurations or candidates that can be used to perform such jobs are generated. A first batch of training jobs can be randomly selected and run using one of the possible resource configuration candidates. Subsequent batches of training jobs may be performed using other resource configuration candidates that have been selected using an optimization process, e.g., Bayesian optimization. Upon reaching a stopping criterion, the resource configuration resulting in a desired optimization metric, e.g., fastest job completion time can be selected and used to execute the remaining training jobs.

Abstract: Systems and methods are provided for updating resource allocation in a distributed network. For example, the method may comprise allocating a plurality of resource containers in a distributed network in accordance with a first distributed resource configuration. Upon determining that a processing workload value exceeds a stabilization threshold of the distributed network, determining a resource efficiency value of the plurality of resource containers in the distributed network. When a resource efficiency value is greater than or equal to the threshold resource efficiency value, the method may generate a second distributed resource configuration that includes a resource upscaling process, or when the resource efficiency value is less than the threshold resource efficiency value, the method may generate the second distributed resource configuration that includes a resource outscaling process. The resource allocation may transmit the second to update the resource allocation.

Abstract: Systems, methods, and computer-readable media are described for employing a machine learning-based approach such as adaptive Bayesian optimization to learn over time the most optimized assignments of incoming network requests to service function chains (SFCs) created within network slices of a 5G network. An optimized SFC assignment may be an assignment that minimizes an unknown objective function for a given set of incoming network service requests. For example, an optimized SFC assignment may be one that minimizes request response time or one that maximizes throughput for one or more network service requests corresponding to one or more network service types. The optimized SFC for a network request of a given network service type may change over time based on the dynamic nature of network performance. The machine-learning based approaches described herein train a model to dynamically determine optimized SFC assignments based on the dynamically changing network conditions.

Abstract: In some examples, a system including physical graphics processing units (GPUs) receives a request to schedule a new job to be executed in the system that is accessible by a plurality of tenants to use the physical GPUs. The system allocates the new job to a collection of vGPUs of the physical GPUs based on an operational cost reduction objective to reduce a cost associated with a usage of the physical GPUs and based on a tenant isolation constraint to provide tenant isolation wherein a single tenant of the plurality of tenants is to use a physical GPU at a time.

Abstract: In hemodynamic determination in medical imaging, the classifier is trained from synthetic data rather than relying on training data from other patients. A computer model (in silico) may be perturbed in many different ways to generate many different examples. The flow is calculated for each resulting example. A bench model (in vitro) may similarly be altered in many different ways. The flow is measured for each resulting example. The machine-learnt classifier uses features from medical scan data for a particular patient to estimate the blood flow based on mapping of features to flow learned from the synthetic data. Perturbations or alterations may account for therapy so that the machine-trained classifier may estimate the results of therapeutically altering a patient-specific input feature. Uncertainty may be handled by training the classifier to predict a distribution of possibilities given uncertain input distribution.

Abstract: A medical knowledge base in a digital, clinical system is upgraded. A storage with a knowledge base, being a SNOMED knowledge base, is provided in a web ontology format. Procedural data, representing clinical procedures for evaluation of a patient's health state, is received. The received procedural data is mapped in a set of SNOMED expressions. The SNOMED expressions are converted into statements in the web ontology format. The SNOMED knowledge base is upgraded with the received procedural data by adding the statements in the SNOMED knowledge base for providing a processable file with an upgraded version of the SNOMED knowledge base.

Abstract: A device and corresponding method are provided determining a consumed computing capacity of a first networking device exceeds the threshold for total capacity for processing monitoring data for a monitoring metric. An optimization engine determines a second networking device with unused computing capacity sufficient for processing the monitoring data generated by the first networking device. The optimization engine automatically moves the monitoring data for the monitoring metric generated by the first networking device to the second networking device and causes the second networking device to process the monitoring data.

Abstract: A technique for determining a cardiac metric from rest and stress perfusion cardiac magnetic resonance (CMR) images is provided. A neural network system for determining at least one cardiac metric from CMR images comprises an input layer configured to receive at least one CMR image representative of a rest perfusion state and at least one CMR image representative of a stress perfusion state. The neural network system further comprises an output layer configured to output at least one cardiac metric based on the at least one CMR image representative of the rest perfusion state and the at least one CMR image representative of the stress perfusion state. The neural network system with interconnections between the input layer and the output layer is trained by a plurality of datasets.

Abstract: Systems and methods for determining corresponding locations of points of interest in a plurality of input medical images are provided. A plurality of input medical images comprising a first input medical image and one or more additional input medical images is received. The first input medical image identifies a location of a point of interest. A set of features is extracted from each of the plurality of input medical images. Features between each of the sets of features are related using a machine learning based relational network. A location of the point of interest in each of the one or more additional input medical images that corresponds to the location of the point of interest in the first input medical image is identified based on the related features. The location of the point of interest in each of the one or more additional input medical images is output.

Abstract: Systems and methods for automated assessment of a vessel are provided. One or more input medical images of a vessel of a patient are received. A plurality of vessel assessment tasks for assessing the vessel is performed using a machine learning based model trained using multi-task learning. The plurality of vessel assessment tasks comprises segmentation of reference walls of the vessel from the one or more input medical images and segmentation of lumen of the vessel from the one or more input medical images. Results of the plurality of vessel assessment tasks are output.