Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates.

Abstract: A system and method to partition pores and throats in 2D or 3D scanned tomographic representations. To achieve partitioning, the methods employ a series of techniques that include the Maximum Inscribed Ball technique, Connected Component Labelling technique, and the 3D Binary Morphology technique.

Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: A method is provided for automatically classifying grains, pores, or both of a formation sample. The method includes receiving a digital image representation of the formation sample, and identifying a plurality of pores, grains, or both in the digital image representation. The method also includes computing a plurality of geometric features associated with the pores, grains, or both in the digital image representation, and inputting the geometric features into an unsupervised machine learning model. The unsupervised machine learning model determines a label for each identified pore and grain, the label being a pore-type or a grain-type, and the plurality of geometric features and the labels determined for each pore, grain, or both, are input into a supervised machine learning model. The supervised machine learning model determines a final classification of a pore-type for each pore and a grain-type for each grain in the digital image representation of the formation sample.

Abstract: A deep learning-based continuous federated learning network system is provided. The system includes a global site comprising a global model and a plurality of local sites having a respective local model derived from the global model. The plurality of model tuning modules having a processing system are provided at the plurality of local sites for tuning the respective local model. The processing system is programmed to receive incremental data and select one or more layers of the local model for tuning based on the incremental data. Finally, the selected layers are tuned to generate a retrained model.

Abstract: Methods and systems are provided for determining scan settings for a localizer scan based on a magnetic resonance (MR) calibration image. In one example, a method for magnetic resonance imaging (MRI) includes acquiring an MR calibration image of an imaging subject, mapping, by a trained deep neural network, the MR calibration image to a corresponding anatomical region of interest (ROI) attribute map for an anatomical ROI of the imaging subject, adjusting one or more localizer scan parameters based on the anatomical ROI attribute map, and acquiring one or more localizer images of the anatomical ROI according to the one or more localizer scan parameters.

Abstract: System and methods of automatic digital rock segmentation are provided. A deep learning model may be trained to segment images of reservoir rock. The training may involve the use of first image data of reservoir rock samples and first segmentation data mapping an intensity of image elements of the first image data to one of a plurality of output channels that respectively represent a characterization of reservoir rock. Second image data of a new reservoir rock sample may be obtained, and an intensity of image elements of the second image data may be determined. Using the trained deep learning model, second segmentation data may be generated that maps the intensity of each image element in the second image data to a corresponding one of the plurality of output channels. The trained deep learning model may output a characterization of the new reservoir rock sample based on the second segmentation data.

Abstract: A system and method to partition pores and throats in 2D or 3D scanned tomographic representations. To achieve partitioning, the methods employ a series of techniques that include the Maximum Inscribed Ball technique, Connected Component Labelling technique, and the 3D Binary Morphology technique.

Abstract: A computer-implemented method of automatically labeling medical images is provided. The method includes clustering training images and training labels into clusters, each cluster including a representative template having a representative image and a representative label. The method also includes training a neural network model with a training dataset that includes the training images and the training labels, and target outputs of the neural network model are labels of the medical images. The method further includes generating a suboptimal label corresponding to an unlabeled test image using the trained neural network model, and generating an optimal label corresponding to the unlabeled test image using the suboptimal label and representative templates.

Abstract: Methods and systems are provided for determining diagnostic-scan parameters for a magnetic resonance (MR) diagnostic-scan, from MR calibration images, enabling acquisition of high-resolution diagnostic images of one or more anatomical regions of interest, while bypassing acquisition of localizer images, increasing a speed and efficiency of MR diagnostic-scanning. In one embodiment, a method for a magnetic resonance imaging (MRI) system comprises, acquiring a magnetic resonance (MR) calibration image of an imaging subject, mapping the MR calibration image to a landmark map using a trained deep neural network, determining one or more diagnostic-scan parameters based on the landmark map, acquiring an MR diagnostic image according to the diagnostic-scan parameters, and displaying the MR diagnostic image via a display device.

Abstract: Product planning techniques are provided that recommend compositions of product features for weighted heterogeneous consumer segments using regression trees. An exemplary method comprises obtaining historical consumer data comprising product preferences for existing product items for multiple consumer segments; obtaining product features indicating characteristics for each existing product item; prioritizing the consumer segments by obtaining a weight indicating an interest in each consumer segment; computing a total performance metric, for each product item, by calculating a dot product between the consumer segment weights and respective preferences of the consumer segments regarding a given product item; obtaining a regression tree from the existing product items to predict the total performance metric in terms of corresponding product features; and selecting a combination of the product features to be used in future product items based on identified paths in the regression tree.

Abstract: Methods and systems are provided for determining scan settings for a localizer scan based on a magnetic resonance (MR) calibration image. In one example, a method for magnetic resonance imaging (MRI) includes acquiring an MR calibration image of an imaging subject, mapping, by a trained deep neural network, the MR calibration image to a corresponding anatomical region of interest (ROI) attribute map for an anatomical ROI of the imaging subject, adjusting one or more localizer scan parameters based on the anatomical ROI attribute map, and acquiring one or more localizer images of the anatomical ROI according to the one or more localizer scan parameters.

Abstract: Methods and apparatus are provided for classifying and discovering historical and future operational states.

Abstract: The present disclosure relates to the classification of images, such as medical images using machine learning techniques. In certain aspects, the technique may employ a distance metric for the purpose of classification, where the distance metric determined for a given image with respect to a homogenous group or class of images is used to classify the image.

Abstract: The present disclosure relates to the classification of images, such as medical images using machine learning techniques. In certain aspects, the technique may employ a distance metric for the purpose of classification, where the distance metric determined for a given image with respect to a homogenous group or class of images is used to classify the image.

Abstract: The present disclosure relates to use of a workflow for automatic prescription of different radiological imaging scan planes across different anatomies and modalities. The automated prescription of such imaging scan planes helps ensure contiguous visualization of the different landmark structures. Unlike prior approaches, the disclosed technique determines the necessary planes using the localizer images itself and does not explicitly segment or delineate the landmark structures to perform plane prescription.

Abstract: Content planning techniques are provided that recommend content features based on the investment interest of advertisers in various audience segments and historical audience measurements. An exemplary method comprises obtaining historical data comprising content preferences indicating a performance metric for each pair of a plurality of content items and audience segment, wherein the content items comprise a plurality of content features indicating characteristics of a corresponding content item; obtaining, for each of a plurality of advertisers, a weight indicating a future interest of a given advertiser in a given audience segment; calculating a pairwise similarity between a vector of the content preferences and a vector of the weights for the plurality of the audience segments to obtain a ranked list of the content items sorted by the pairwise similarity; and generating a summarization of the content features to be used in future content items based on the ranked list.

Abstract: Methods and apparatus are provided for domain-tailored detection of outliers, patterns, and/or events in data streams. An exemplary method comprises obtaining a domain-dependent definition of (i) data outliers based on predefined outlier criteria; (ii) data patterns based on predefined pattern criteria; and/or (iii) data events based on predefined event criteria; obtaining time series measurement data from a plurality of sensors; determining, substantially simultaneously with the obtaining, whether individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events; and storing the individual samples with an indication of whether the individual samples satisfy the domain-dependent definitions of the data outliers, data patterns and/or data events. The domain-dependent definitions are optionally specified using a declarative command language.

Abstract: Techniques are provided for automatic combination of sub-process simulation results with dataset selection based on a fitness under one or more specific scenarios. An exemplary method comprises obtaining an execution map for each sub-process in a sequence that stores results of a given sub-process execution. The results comprise a scenario, a distribution and a distribution fitness value. In response to a user query regarding a target feature and an initial dataset, initial dataset are combined with results selected from the execution map for a first sub-process in the sequence; each available dataset from the previous sub-processes in the sequence is combined with results selected from the execution map for the next sub-process; a probability distribution function (pdf) for the target feature is composed from a combined dataset that represents a simulation of the process and combines results of each of sub-process in the sequence; and the pdf is processed to answer the user query for the target feature.