Abstract: Since the final output for medical imaging is the radiology report, the quality of which is largely dependent on the radiologist, there is a need for a comprehensive system for both medical imaging and reporting. Imaging and radiology reporting are combined. Image acquisition, reading of the images, and reporting are linked, allowing feedback of readings to control acquisition so that the final reporting is more comprehensive. Clinical findings typically associated with reporting may be used automatically to feedback for further or continuing acquisition without requiring a radiologist. A clinical identification may be used to determine what image processing to perform for reading, and/or raw (i.e., non-reconstructed) scan data from the imaging system are provided for integrated image processing with report generation.

Abstract: A method and system for fast non-invasive computer-based computation of a hemodynamic index, such as fractional flow reserve (FFR) from medical image data of a patient is disclosed. A patient-specific anatomical model of one or more arteries of a patient is automatically generated based on medical image data of the patient. Regions in the automatically generated patient-specific anatomical model for which user feedback is required for accurate computation of a hemodynamic index are predicted using one or more trained machine learning models.

Abstract: Example implementations relate to a proactive auto-scaling approach. According to an example, a machine-learning prediction model is trained to forecast future serverless workloads during a window of time for an application running in a public cloud based on past serverless workload information associated with the application by performing a training process. During the window of time, serverless workload information associated with the application is monitored. A future serverless workload is predicted for the application at a future time within the window, based on the machine learning prediction model. Prior to the future time, containers within the public cloud executing the application are pre-warmed to accommodate the predicted future serverless workload by issuing fake requests to the application to trigger auto-scaling functionality implemented by the public cloud.

Abstract: Methods and systems for artificial intelligence based medical image segmentation are disclosed. In a method for autonomous artificial intelligence based medical image segmentation, a medical image of a patient is received. A current segmentation context is automatically determined based on the medical image and at least one segmentation algorithm is automatically selected from a plurality of segmentation algorithms based on the current segmentation context. A target anatomical structure is segmented in the medical image using the selected at least one segmentation algorithm.

Abstract: An optical network includes top networking ports coupled to a packet switch, first media converters, second media converters, and bottom networking ports. The first media converters are coupled to top networking ports, each of the first media converters including a first ASIC transceiver that has a circuit switch function. The second media converters are coupled to the first media converter via optical cables to receive the optical signals. Each of the second media converters includes a second ASIC transceiver that has a circuit switch function. The bottom networking ports are coupled to the second media converters. The first ASIC transceiver and the second ASIC transceiver are configured to transmit a signal from one of the top networking ports to any one of the bottom networking ports, and transmit a signal from one of the bottom networking ports to any one of the top networking ports.

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: Systems and methods are provided for managing network devices using policy graph representations. In some embodiments, the method includes receiving configurations for a plurality of network devices; extracting one or more policies from the configurations; extracting a label hierarchy from the configurations, the label hierarchy describing an organization of nodes in a network comprising the network devices; generating a connectivity of a network comprising the network devices based on the one or more policies and the label hierarchy; generating a policy graph representation of the connectivity of the network; and displaying the policy graph representation of the connectivity to a user.

Abstract: Systems and methods are provided for improving autotuning procedures using stateless processing with a remote key-value store. For example, the system can implement a task launcher, a scheduler, and an agent to launch, schedule, and execute decomposed autotuning stages, respectively. The scheduling policy implemented by the scheduler may perform operations beyond a simple scheduling policy (e.g., a FIFO-based scheduling policy), which produces a high queuing delay. Compared to the traditional systems, by leveraging autotuning specific domain knowledge, queueing delay is reduced and resource utilization is improved.

Abstract: Embodiments of the present disclosure provide for asset lifetime monitoring. Estimated values for factors representing root causes of operational anomalies of an asset may be generated utilizing one or more models. Estimated remaining lifetime values for one or more root cause variables may be generated that indicate a time until the value for a root cause variable is estimated to reach a particular limit threshold corresponding to the root cause variable, and/or an estimated second remaining lifetime value for an asset health index representing a combination of one or more root cause variables. The second remaining lifetime value for the asset health index may be provided to enable processing of the second remaining lifetime value as the remaining useful lifetime of the asset based on overall root cause variables.

Abstract: Techniques and architectures for measuring available bandwidth. A train of probe packets is received from a remote electronic device. A received time for a first subset of the train of probe packets is measured. A change in capacity of the network path is determined based on the measured received time for the first subset of packets. Packets from the train of probe packets prior to the detected change in capacity of the network path are excluded to identify a second subset of packets. An estimated available bandwidth is computed based on the second subset of packets from the train of probe packets.

Abstract: A natural language sentence is generated for a radiology report. One or more words are obtained where the one or more words were produced based on image processing of a radiology image. A computer implemented text analysis process is used to analyse the one or more words to generate a natural language sentence representing the radiology image. The natural language sentence is output. The computer implemented text analysis process includes: determining, for each of the one or more words, and using word modified embeddings, a vector representing the word; and determining, based on the determined one or more vectors, and using a text generator model, the natural language sentence.

Abstract: A method and system for automated disease progression modeling and therapy optimization for an individual patient is disclosed. A current condition of the patient is modeled using a state-variable model in which a plurality of state variables in a state vector represent a plurality of characteristics of the patient. Disease progression for the patient is predicted based on the state variables of the patient. An optimization is performed to determine an optimal therapy type and an optimal therapy timing for the patient based on the predicted disease progression for the patient.

Abstract: Systems and methods are provided for training an artificial intelligence model for detecting calcified portions of a vessel in an input medical image. One or more first medical images of a vessel in a first modality and one or more second medical image of the vessel in a second modality are received. Calcified portions of the vessel are detected in the one or more first medical images, The artificial intelligence model is trained for detecting calcified portions of the vessel in the input medical image in the second modality based on the one or more second medical images and the detected calcified portions of the vessel detected in the one or more first medical images.

Abstract: Systems and methods for determining myocardium strain and intracardiac blood flow data of the heart are provided. Input medical imaging data of a heart of a patient is received. At least one of extracted myocardium strain data of the heart and extracted intracardiac blood flow data of the heart is determined from the input medical imaging data. At least one of predicted myocardium strain data of the heart and predicted intracardiac blood flow data of the heart is determined based on the at least one of the extracted myocardium strain data and the extracted intracardiac blood flow data using a model of the heart. The model of the heart jointly models myocardium strain of the heart and intracardiac blood flow of the heart. The at least one of the predicted myocardium strain of the heart and the predicted intracardiac blood flow of the heart is output.

Abstract: Systems and methods for predicting a location for acquiring a target view of an anatomical object of interest in an input image are provided. An input image of an anatomical object of interest of a patient is received. An output image is generated using a machine learning based network. The output image depicts a projection of a 3D image plane for acquiring a target view of the anatomical object of interest identified on the input image. The output image is output.

Abstract: Systems and methods are provided for improving autotuning procedures. For example, the system can implement a task launcher, a scheduler, and an agent to launch, schedule, and execute decomposed autotuning stages, respectively. The scheduling policy implemented by the scheduler may perform operations beyond a simple scheduling policy (e.g., a FIFO-based scheduling policy), which produces a high queuing delay. By leveraging autotuning specific domain knowledge, this may help reduce queuing delay and improve resource utilization that is otherwise found in traditional systems.

Abstract: Methods for computing hemodynamic quantities include: (a) acquiring angiography data from a patient; (b) calculating a flow and/or calculating a change in pressure in a blood vessel of the patient based on the angiography data; and (c) computing the hemodynamic quantity based on the flow and/or the change in pressure. Systems for computing hemodynamic quantities and computer readable storage media are described.

Abstract: Systems and methods for generating a synthesized contrast enhanced medical image are provided. An input medical image is received. A synthesized contrast enhanced medical image is generated based on the input medical image using a trained machine learning based generator network. The synthesized contrast enhanced medical image includes one or more synthesized contrast enhanced regions of pathological tissue. The synthesized contrast enhanced medical image is output.

Abstract: Example implementations relate to a proactive auto-scaling approach. According to an example, a target performance metric for an application running in a serverless framework of a private cloud is received. A machine-learning prediction model is trained to forecast future serverless workloads during a window of time for the application based on historical serverless workload information. The serverless framework is monitored to obtain serverless workload observations for the application. A future serverless workload for the application at a future time is predicted by the trained machine learning prediction model based on workload observations. A feedback control system is then used to output a new number of replicas based on a current value of the performance metric, the target performance metric and the predicted future serverless workload. Finally, the serverless framework is caused to scale and pre-warm a number of replicas supporting the application to the new number.

Abstract: An optical network includes top networking ports coupled to a packet switch, first media converters, second media converters, and bottom networking ports. The first media converters are coupled to top networking ports, each of the first media converters including a first ASIC transceiver that has a circuit switch function. The second media converters are coupled to the first media converter via optical cables to receive the optical signals. Each of the second media converters includes a second ASIC transceiver that has a circuit switch function. The bottom networking ports are coupled to the second media converters. The first ASIC transceiver and the second ASIC transceiver are configured to transmit a signal from one of the top networking ports to any one of the bottom networking ports, and transmit a signal from one of the bottom networking ports to any one of the top networking ports.