Abstract: For movement of medical sensors for medical imaging, an artificial intelligence (AI) is trained using a contrastive reinforcement learning (CRL) framework. Simulation may be used to provide the training data. For training, the sampling for a given input instance in CRL may use trajectories simulated from different patients for better contrast. CRL, with or without the simulation feature and/or the sampling feature, may provide more generalized navigation, such as in interventional or diagnostic settings.

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.

Abstract: Methods, devices, and systems for aircraft identification are described herein. In some examples, one or more embodiments include a computing device comprising a memory and a processor to execute instructions stored in the memory to simulate virtual light detection and ranging (Lidar) sensor data for a three-dimensional (3D) model of an aircraft type to generate a first point cloud corresponding to the 3D model of the aircraft type, generate a classification model utilizing the simulated virtual Lidar sensor data of the 3D model of the aircraft type, and identify a type and/or sub-type of an incoming aircraft at an airport by receiving, from a Lidar sensor at the airport, Lidar sensor data for the incoming aircraft, generating a second point cloud corresponding to the incoming aircraft utilizing the Lidar sensor data for the incoming aircraft, and classifying the second point cloud corresponding to the incoming aircraft using the classification model.

Abstract: In certain embodiments, a computer-implemented method includes: receiving, by a caching system plugin, a request to create a persistent volume for a container application instance; configuring, by the caching system plugin, a local cache volume on a host computing device; configuring, by the caching system plugin, a remote storage volume on a remote storage device; selecting, by a policy manager of the caching system plugin, a cache policy for the container application instance; creating, by the caching system plugin and from a cache manager, a virtual block device associated with the local cache volume, the remote storage volume, and the cache policy; and providing the virtual block device for use by the container application instance as the persistent volume.

Abstract: Systems and methods for performing a medical imaging analysis task are provided. An input medical image in a first modality is received. Features are extracted from the input medical image using a first machine learning based encoding network. A medical imaging analysis task is performed on the input medical image based on the extracted features by, in one embodiment, decoding the extracted features to generate results of the medical imaging analysis task using a machine learning based decoding network. Results of the medical imaging analysis task are output. In one embodiment, the first machine learning based encoding network is jointly trained with a second machine learning based encoding network with an unsupervised loss using unannotated pairs of training images. Each of the unannotated pairs comprise a first training image in the first modality and a second training image in a second modality.

Abstract: Systems and methods are provided for a multi-tenant collective communication fabric for optimal utilization of communication links between compute nodes of an interconnected system. Examples include allocating a plurality of compute nodes to a first workload and obtaining a topology of the interconnected system representative of an indirect path between the allocated compute nodes. The indirect path comprises a non-allocated compute node of the interconnected system. The examples include creating plurality of slices of resources of the non-allocated compute node, with a first slice dedicated to processing and forwarding data traffic along the indirect path and a second slice configured for allocation to a second workload. The examples also include executing the first workload by the allocated compute nodes and the non-allocated compute node, wherein data traffic from the plurality of allocated compute nodes is communicated via the indirect communication path.

Abstract: Systems and methods for real time three-dimensional left atrial appendage (LAA) quantification. Deep learning is used to perform LAA segmentation, tracking and live quantification on critical measurement in 4D ultrasound sequences. The collected information may be used for device selection and anomaly detection to assist early-phase disease finding.

Abstract: Systems and methods for assessing cardiovascular disease of a patient are provided. Patient data of the patient is received. The patient data may include one or more input medical images of a chest of the patient, results of an assessment of a lung disease performed based on the one or more input medical images, demographic and clinical data of the patient, and cardiovascular imaging exams of the patient. One or more risk scores are computed for the patient based on the patient data using a trained machine learning based network.

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: 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 are provided for effectuating a filtering technique that can enable available bandwidth (e.g., on a network path) to be estimated in the presence of moderate losses caused by certain queue management techniques. When packet losses exist on a network path due to certain types of packet queue transmission mechanisms, methods, or models, a bump detection algorithm (BDA) can be used to perform bandwidth estimation. When a pattern of packet loss is identified (from its signature) as being one with which BDA can be performed to accurately estimate available bandwidth on a network path, the BDA-based bandwidth estimate may be used to place/route and load balance network traffic, or otherwise used to engage in network traffic engineering, take other network-related action(s), or reported out. Otherwise, the bandwidth estimation is suppressed and not used.

Abstract: Systems and methods for automatic assessment of a lesion are provided. One or more input medical images of a vessel of a patient is received. A lesion is defined in the one or more input medical images. A region of interest around the lesion is defined in the one or more input medical images. Radiomic features are extracted from the region of interest. An assessment of the lesion is determined using a machine learning based classifier network based on the radiomic features. The assessment of the lesion is output.

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: In certain embodiments, a method includes receiving, at an interface of a Smart network interface card (SmartNIC) of a computing device, via a network, a network data unit; processing, by a data allocator of a SmartNIC subsystem of the SmartNIC, the network data unit to make a determination that data included in the network data unit is intended for processing by an accelerator of the computing device, wherein the accelerator is configured to execute a machine learning algorithm; storing, by the data allocator and based on the determination, the data in a local buffer of the SmartNIC subsystem; identifying, by the data allocator, a memory resource associated with the accelerator; and transferring the data from the local buffer to the memory resource.

Abstract: In certain embodiments, a computer-implemented method includes: receiving, by a caching system plugin, a request to create a persistent volume for a container application instance; configuring, by the caching system plugin, a local cache volume on a host computing device; configuring, by the caching system plugin, a remote storage volume on a remote storage device; selecting, by a policy manager of the caching system plugin, a cache policy for the container application instance; creating, by the caching system plugin and from a cache manager, a virtual block device associated with the local cache volume, the remote storage volume, and the cache policy; and providing the virtual block device for use by the container application instance as the persistent volume.

Abstract: Techniques for processing multiple cardiac images are disclosed. The multiple cardiac images, each of which depicts a portion of coronary arteries, i.e., the same portion of coronary arteries, within an anatomical region of interest, are obtained either during or after an angiography exam. A respective set of lumen radius measurements is determined based on each of the multiple cardiac images and comprises multiple lumen radius measurements respectively associated with multiple locations of the portion of the coronary arteries. A maximum stenosis severity profile and a minimum stenosis severity profile associated with the portion of the coronary arteries are respectively determined based on the respective sets of lumen radius measurements. A lumen radius profile associated with the portion of the coronary arteries is determined based on the maximum stenosis severity profile and the minimum stenosis severity profile.

Abstract: In certain embodiments, a method includes receiving, at an interface of a Smart network interface card (SmartNIC) of a computing device, via a network, a network data unit; processing, by a data allocator of a SmartNIC subsystem of the SmartNIC, the network data unit to make a determination that data included in the network data unit is intended for processing by an accelerator of the computing device, wherein the accelerator is configured to execute a machine learning algorithm; storing, by the data allocator and based on the determination, the data in a local buffer of the SmartNIC subsystem; identifying, by the data allocator, a memory resource associated with the accelerator; and transferring the data from the local buffer to the memory resource.

Abstract: Systems and methods for generating a response summarizing patient data are provided. One or more prompts, comprising 1) patient data retrieved from one or more patient databases and 2) instructions, are received. A response summarizing the patient data is generated based on the instruction using a large language model. The response is output.

Abstract: Systems and methods for performing one or more medical imaging analysis tasks are provided. A sequence of medical images is received. One or more patches are extracted from each image of the sequence of medical images. Spatio-temporal features are extracted from the one or more extracted patches using a machine learning based encoder network. One or more medical imaging analysis tasks are performed based on the extracted spatio-temporal features. Results of the one or more medical imaging analysis tasks are output. The machine learning based encoder network is trained by receiving a sequence of training medical images. Patches of a first set of images of the sequence of training medical images are masked according to a first masking strategy. Patches of a second set of images of the sequence of training medical images are masked according to a second masking strategy.

Abstract: A computer-implemented method for multi-modality medical image clinical workflow guidance comprises a step of receiving a user input (306-4) in relation to a first medical image data set (306-2) acquired by means of a first medical imaging modality (304-A). By means of a clinical-concept-to-medical-image linking algorithm (314), the received user input (306-4) is assessed in view of at least one second medical image data set (308-1) acquired by means of at least one second medical imaging modality (304-B). An indication of a clinical workflow guidance in relation to the at least one second medical image data set (308-1) is output.