Abstract: An iterative framework for learning multimodal mappings tailored to medical image inferencing tasks is provided. In an example, a computer-implemented method can comprise receiving multimodal annotation data for medical images, the multimodal annotation data comprising non-image annotation data and image annotation data, and employing one or more machine learning (ML) processes to learn bi-directional mappings between non-image features included in the non-image annotation data and image features associated with the medical images and the image annotation data. The method further comprises generating, as a result of the one or more ML processes, a model configured to: infer one or more of the non-image features associated with new medical images given the new medical images, and/or infer one or more of the image features associated with the new medical images given the new medical images and non-image input corresponding to at least some of the non-image annotation data.

Abstract: Systems or techniques that facilitate ensembled querying of example images via deep learning embeddings are provided. In various embodiments, a system can access a medical image associated with a medical patient. In various aspects, the system can generate an ensembled heat map indicating where in the medical image an anatomical structure is likely to be located, by executing an embedder neural network on the medical image and on a plurality of example medical images associated with other medical patients. In various instances, respective instantiations of the anatomical structure can be flagged in the plurality of example medical images by user-provided clicks.

Abstract: Methods and Systems are provided for performing early onset diagnostics for clogged injectors using fuel trims collected from the Engine Management System and evaluated in different operating modes of the engine. Example implementations use fuel trims that can estimate the contrast in combustion ratio ? across different operating modes defined on the engine speed and calculated engine load plane. Features are extracted, then classified using machine learning to output a diagnosis according to different levels of fuel injector clogging.

Abstract: Techniques are described for maintaining patient privacy in association with obtaining patient data for machine learning applications. In an example, a method can comprise accessing, by a system comprising a processor, a training dataset associated with a machine learning model, the training dataset comprising of data samples respectively comprising unique characteristics of subjects. The method further comprises determining, by the system, characteristic curve information that correlates different data portions extracted from the data samples to respective probabilities of matching the different data portions to respective ones of the data samples from which they are extracted.

Abstract: Systems/techniques that facilitate improved uncertainty estimation via object-specific and object-agnostic segmentation disagreement are provided. In various embodiments, a system can access an image depicting an object. In various aspects, the system can localize, via execution of an object-specific segmentation model on the image, a first inferred boundary of the object. In various instances, the system can generate an uncertainty score for the first inferred boundary, based on a second inferred boundary of the object generated via execution of an object-agnostic segmentation model on the image.

Abstract: Techniques are described for refining or updating medical image inferencing models post deployment using synthetic images generated from non-image data feedback. In an example, a system can comprise a memory that stores computer-executable components and a processor that executes the computer-executable components stored in the memory. The computer-executable components can comprise an image generation component that generates synthetic medical images based on feedback information associated with performance of a medical image inferencing model received in association with application of the medical image inferencing model to medical images in a deployment environment, wherein the feedback information excludes image data. The computer-executable components can further comprise a refinement component that updates the medical image inferencing model using the synthetic images and a model updating process.

Abstract: Techniques are described that facilitate training an artificial intelligence (AI) model in a manner that ensures equitable model performance across different sub-groups. According to an embodiment, a system is provided that includes a memory that stores computer executable components, and a processor that executes the computer executable components stored in the memory. The computer executable components include a training component that trains a machine learning (ML) model on training data to perform an inferencing task using an equitable loss function that drives equitable performance of the ML model across different sub-groups represented by the training data, resulting in trained version of the ML model that provides a defined equitable performance level across the different sub-groups. The equitable loss function is “sub-group aware” and penalizes variation in model performance across the sub-groups during model training and validation.

Abstract: Systems and methods are provided for super resolution for electronic 4D (e4D) cardiovascular ultrasound (CVUS) probes. In a medical imaging system, signals associated with a medical imaging technique may be acquired and processed, with the processing including applying one or both of a first type of correction to address a first type of degradation and a second type of correction to address a second type of degradation, with the first type of degradation being based on or caused by sparse acquisition, and the second type of degradation being based on or caused by choice of beamforming/reconstruction methodology.

Abstract: Systems/techniques that facilitate task-specific image style transfer are provided. In various embodiments, a system can access a first medical image, wherein the first medical image can exhibit anatomical content and a first visual style. In various aspects, the system can generate, via execution of an optimization algorithm, a second medical image based on the first medical image, wherein the second medical image can exhibit the anatomical content and a second visual style that is different from the first visual style. In various instances, the optimization algorithm can be based on feature maps extracted from a pre-trained deep learning neural network that has been configured to perform an inferencing task on medical images exhibiting the second visual style.

Abstract: Various methods and systems are provided for segmenting a three-dimensional (3D) region of interest (ROI) from an image sequence. In one embodiment, a method includes receiving a positive point selection or negative point selection for an ROI in a first image slice of an image sequence, mapping the first image slice and the positive point selection or negative point selection to a first segmentation mask of the ROI using a weakly supervised segmentation model, simulating positive and negative point selections in a second image slice based on the first segmentation mask to produce a plurality of simulated positive and negative point selections, wherein the second image slice is adjacent to the first image slice, and mapping the second image slice and the plurality of simulated positive and negative point selections to a second segmentation mask for the ROI in the second image slice using the weakly supervised segmentation model.

Abstract: Systems/techniques that facilitate variable confidence machine learning are provided. In various embodiments, a system can access a medical image. In various aspects, the system can perform, via execution of a machine learning model, a regression task on the medical image, wherein the machine learning model can receive as input both the medical image and a user-specified confidence indicator.

Abstract: Systems and methods are disclosed for analyzing traffic received at a network visibility node to determine traffic levels relative to capacity at tools communicatively coupled to the network visibility node and throttling traffic when the traffic levels exceed tool capacity. In an illustrative embodiment, streams received at a network visibility node are analyzed to predict a traffic level) for a given traffic flow. The predicted level of traffic for a given traffic flow is used to decide whether to forward traffic associated with the given traffic flow to a tool port of the network visibility node that is communicatively coupled to an external tool.

Abstract: Systems/techniques that facilitate machine learning image analysis based on explicit equipment parameters are provided. In various embodiments, a system can access a medical image generated by a medical imaging device. In various instances, the system can perform, via execution of a machine learning model, an inferencing task on the medical image. In various cases, the machine learning model can receive as input the medical image and a set of equipment parameters. In various aspects, the set of equipment parameters can indicate how the medical imaging device was configured to generate the medical image.

Abstract: Systems and methods are disclosed for analyzing traffic received at a network visibility node to determine traffic levels relative to capacity at tools communicatively coupled to the network visibility node and throttling traffic when the traffic levels exceed tool capacity. In an illustrative embodiment, streams received at a network visibility node are analyzed to predict a traffic level) for a given traffic flow. The predicted level of traffic for a given traffic flow is used to decide whether to forward traffic associated with the given traffic flow to a tool port of the network visibility node that is communicatively coupled to an external tool.

Abstract: Systems and methods are disclosed for analyzing traffic received at a network visibility node to determine traffic levels relative to capacity at tools communicatively coupled to the network visibility node and throttling traffic when the traffic levels exceed tool capacity. In an illustrative embodiment, streams received at a network visibility node are analyzed to predict a traffic level for a given traffic flow. The predicted level of traffic for a given traffic flow is used to decide whether to forward traffic associated with the given traffic flow to a tool port of the network visibility node that is communicatively coupled to an external tool.

Abstract: Embodiments allow, within database security policies, the grant of data change operation-specific privileges to particular users to be applied within particular data realms in a given table. Furthermore, according to one or more embodiments, User Privilege column-level privileges are explicitly associated with one or more data access operations such that the grant of such a column-level privilege allows the user to perform only those data access operations that are explicitly associated with the column-level privilege. Enforcement of the data security policies includes prevention of data leakage via WHERE and RETURNING INTO clauses. According to one or more embodiments, a two-phase rewrite is used to optimize enforcement of column-level privileges. During the two-phase rewrite of a given query, the privileges checked during enforcement of the User Privilege data security policies are pruned to avoid unnecessary privilege checks given the columns that are accessed in the query.

Abstract: Briefly, embodiments of a system, method, and article for transmitting, from a first application, a query for a domain entity associated with a second application. A transformation microservice may receive change data indicating modifications to the domain entity associated with the second application. The second application may execute the query on data for the domain entity associated with the first application and may generate a first output file with query results. The transformation microservice may execute the query on the change data for the domain entity associated with the second application and generating a second output file with query results. The first output file and the second output file may be sent to a comparison microservice. The comparison microservice may determine whether there are differences between the query results of the first output file and the second output file.

Abstract: Briefly, embodiments of a system, method, and article for transmitting, from a first application, a query for a domain entity associated with a second application. A transformation microservice may receive change data indicating modifications to the domain entity associated with the second application. The second application may execute the query on data for the domain entity associated with the first application and may generate a first output file with query results. The transformation microservice may execute the query on the change data for the domain entity associated with the second application and generating a second output file with query results. The first output file and the second output file may be sent to a comparison microservice. The comparison microservice may determine whether there are differences between the query results of the first output file and the second output file.

Abstract: A server automatically generates a response to a request received from a first user. Configuration information is received from second users. A request is received from the first user. A first stage of matching the parameters of the request to the second users is performed. If the number of matching users is below a threshold, the first stage of matching is performed again expanding or loosening the requirements of the request. A second stage of matching is performed that includes computing a score, and second user(s) are selected based in part on the computed score.

Abstract: Described herein are systems and methods for coupling environmental hazard detection and actuation of a wearable chemical sensor at a molecular level. The chemical sensor may be a wearable chemical sensor implemented as a powder, cream, lacquer or other wearable construct. The wearable chemical sensor may detect exposure to various environmental hazards and provide an analog means (e.g., a range of color changes) of indicating the level of environmental hazard exposure.