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 are provided for strategically harvesting untapped compute capacity of hardware accelerators to manage transient workload spikes at computing systems, are provided. Examples provide a low-cost and scalable computing system which orchestrates seamless offloading of workloads to hardware accelerators during transient workload spikes. By utilizing hardware accelerators as short-term emergency buffers, examples improve upon existing approaches which deploy more expensive, and often significantly under-utilized servers for these emergency purposes. Accordingly, examples may reduce the occurrence of SLA violations while minimizing capital expenditure in computing power.

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 and methods for determining a robustness of a machine learning based medical analysis network for performing a medical analysis task on input medical data are provided. Input medical data is received. Results of a medical analysis task performed based on the input medical data using a machine learning based medical analysis network are received. A robustness of the machine learning based medical analysis network for performing the medical analysis task is determined based on the input medical data and the results of the medical analysis task using a machine learning based audit network. The determination of the robustness of the machine learning based medical analysis network is output.

Abstract: Systems and methods for smart selection of training data sets by using clinically driven application dependent evaluation metrics to assess the performance of deep learning models after deployment in the field. A machine trained model is deployed to a clinical environment. An evaluation metric is acquired that correlates with a clinical outcome for each instance of the machine trained model performing the task for a medical procedure. Data sets are flagged that are challenging for the machine trained model based on the evaluation metrics. The flagged data sets are prioritized during retraining of the machine trained model.

Abstract: Systems and methods for determining an assessment of a patient for a medical condition are provided. Input medical data of a patient is received. A knowledge graph is computed based on the input medical data. A vector representing a state of the patient is generated based on the knowledge graph. An assessment of the patient for a medical condition is determined using a machine learning based network based on the vector. The assessment of the patient is output.

Abstract: Systems and methods for determining an assessment of a patient for a medical condition are provided. Input medical data of a patient is received. A vector representing a state of the patient is generated based on the input medical data. An assessment of the patient for a medical condition is determined using a machine learning based network based on the vector. The assessment of the patient is output.

Abstract: Systems and methods for graph based assessment of a patient are provided. Medical imaging data and non-imaging medical data of a patient are received. The medical imaging data and the non-imaging medical data are encoded into encoded features using a graph based machine learning network by comparing the patient with patients of a patient population based on a graph of the patient population. An assessment of the patient is determined based on the encoded features using a machine learning classifier network. The assessment of the patient 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.

Abstract: Patient, user, and/or AI information are used in a multi-objective optimization to select one of a plurality of available AIs for a task. On a patient or user-specific basis, an optimal AI is selected and applied for medical imaging or other healthcare actions. The selection may be before application, avoiding costs of applying multiple AIs to obtain the best results. The optimization may be based on statistical feedback from the user for various of the available AIs, providing information not otherwise available. The optimization may be based on AI performance, AI inclusion and/or exclusion criteria, and/or pricing information. By using optimization based on various information related to the patient, user, and/or available AI, the application of AI for a given user and/or patient by the computer may be improved. The computer operates better to provide more focused information through AI application.

Abstract: Example implementations relate to consensus protocols in a stretched network. According to an example, a distributed system includes continuously monitoring network performance and/or network latency among a cluster of a plurality of nodes in a distributed computer system. Leadership priority for each node is set based at least in part on the monitored network performance or network latency. Each node has a vote weight based at least in part on the leadership priority of the node. Each node's vote is biased by the node's vote weight. The node having a number of biased votes higher than a maximum possible number of votes biased by respective vote weights received by any other node in the cluster is selected as a leader node.

Abstract: Embodiments described herein are generally directed to an edge-CaaS (eCaaS) framework for providing life-cycle management of containerized applications on the edge. According to an example, declarative intents are received indicative of a use case for which a cluster of a container orchestration platform is to be deployed within an edge site that is to be created based on infrastructure associated with a private network. A deployment template is created by performing intent translation on the declarative intents and based on a set of constraints. The deployment template identifies the container orchestration platform selected by the intent translation. The deployment template is then executed to deploy and configure the edge site, including provisioning and configuring the infrastructure, installing the container orchestration platform on the infrastructure, configuring the cluster within the container orchestration platform, and deploying a containerized application or portion thereof on the cluster.

Abstract: For training for and performance of LGE analysis, multi-task machine-learning model is trained to output various cardiac tissue characteristics based on input of LGE MR data. The use of segmentation may be avoided or limited, resulting in a greater number of available training data samples, by using radiology clinical reports with LGE information as a source for samples. The multi-task model may be trained to output cardiac tissue characteristics using radiology clinical reports with LGE information with no segmentation or with segmentation for only a subset of the training samples. By training for multiple tasks, the accuracy of prediction for each task benefits from the information for other tasks. The trained model outputs values of characteristics for multiple tasks, such as extent of enhancement, type of enhancement, and localization of enhancement. Other tasks may be included, such as disease classification. Other inputs may be used, such as also including sensor data and/or cardiac motion.

Abstract: Systems and methods are provided for a new type of quality of service (QoS) primitive at a network device that has better performance than traditional QoS primitives. The QoS primitive may comprise a token bucket with active queue management (TBAQM). Particularly, the TBAQM may receive a data packet that is processed by the token bucket; adjust tokens associated with the token bucket, where the tokens are added based on a configured rate and subtracted in association with processing the data packet; determine a number of tokens associated with the token bucket, comprising: when the token bucket has zero tokens, initiating a first action with the data packet, and when the token bucket has more than zero tokens, determining a marking probability based on the number of tokens and initiating a second action based on the marking probability.

Abstract: System and method for safe over-the-air (OTA) update of electronic control units in vehicles are provided. The method includes checking whether a vehicle condition allows firmware update of an electronic control unit in a vehicle. If the vehicle condition allows the firmware update, the method includes causing a telematics device to complete the firmware update for the electronic control unit.

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 are performed by the machine learning based model based on shared features extracted from the one or more input medical images. Results of the plurality of vessel assessment tasks or a combination of the results of the plurality of vessel assessment tasks are output.

Abstract: Systems and methods for navigating a catheter in a patient using a robotic navigation system with risk management are provided. An input medical image of a patient is received. A trajectory for navigating a catheter from a current position to a target position in the patient is determined based on the input medical image using a trained segmentation network. One or more actions of a robotic navigation system for navigating the catheter from the current position towards the target position and a confidence level associated with the one or more actions are determined by a trained AI (artificial intelligence) agent and based on the generated trajectory and a current view of the catheter. In response to the confidence level satisfying a threshold, the one or more actions are evaluated based on a view of the catheter when navigated according to the one or more actions.

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: An example system may comprise a first computing device comprising instructions executable by a hardware processor to: create, responsive to detecting a second computing device initially attempting to connect to a network, an unpopulated baseline profile for the second computing device; populate the baseline profile with initial processes running on the second computing device and initial system calls made by the initial processes during an initial operation time period of the second computing device; monitor, during a subsequent operation time period of the second computing device, subsequent processes running on the second computing device and subsequent system calls made by the subsequent processes; and detect an attack on the second computing device based on a comparison of the subsequent processes and the subsequent system calls to the populated baseline profile.

Abstract: Example implementations relate to scheduling of jobs for a plurality of graphics processing units (GPUs) providing concurrent processing by a plurality of virtual GPUs. According to an example, a computing system including one or more GPUs receives a request to schedule a new job to be executed by the computing system. The new job is allocated to one or more vGPUs. Allocations of existing jobs are updated to one or more vGPUs. Operational cost of operating the one or more GPUs and migration cost of allocating the new job are minimized and allocations of the existing jobs on the one or more vGPUs is updated. The new job and the existing jobs are processed by the one or more GPUs in the computing system.