Emc IP Holding Company LLC

Emc Corporation

General Electric Company

Ge Precision Healthcare LLC

André De Almeida Maximo

Angelo E M Ciarlini

Jonas Furtado Dias

Adriana Bechara Prado

Chitresh Bhushan

Thomas Kwok-Fah Foo

Dattesh Dayanand Shanbhag

Vinícius Michel Gottin

Desmond Teck Beng Yeo

Victor Bursztyn

Monica Barros

Vinicius Michel Gottin

Rômulo Teixeira De Abreu Pinho

Rakesh Mullick

Alex Laier Bordignon

Dawei Gui

Soumya Ghose

Arathi Sreekumari

Edward José Pacheco Condori

Diego Salomone Bruno

Jaumir Valença Da Silveira Junior

Radhika Madhavan

Rodrigo Dias Arruda Senra

Luiz Guilherme Oliveira Dos Santos

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.

Plane selection using localizer images

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. In addition, the method includes updating the training dataset using the test image and the optimal label, retraining the neural network model, generating a label of an unlabeled image using the retrained neural network model, and outputting the generated label.

Systems and methods of automatic medical image labeling

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.

Systems and methods for generating localizer scan settings from calibration images

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.

Method, medium, and system for recommending compositions of product features using regression trees

Methods and apparatus are provided for classifying and discovering historical and future operational states. An exemplary method comprises obtaining historical Boolean sensor data from sensors for a given time; generating a plurality of signatures over time comprised of the historical Boolean sensor data from the sensors for a corresponding given time; determining a plurality of time intervals having a corresponding interval signature, wherein each time interval is comprised of consecutive time units having a substantially same signature; assigning a predefined state to each time interval based on the interval signatures using a clustering method; obtaining, for each time interval, historical numerical sensor data from the sensors corresponding to measurements of the sensors during the associated time interval; and training a model using the historical numerical sensor data as an input, and the predefined state assigned to each time interval as a target output, to obtain coefficients of the machine learning model.

Methods and apparatus for classifying and discovering historical and future operational states based on Boolean and numerical sensor data

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.

System and method for assessing image quality

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.

Recommending features for content planning based on advertiser polling and historical audience measurements

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.

Automatic combination of sub-process simulation results with dataset selection based on fitness under specific scenarios

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. Query are optionally processed comprising one or more declarative statements that reference and/or manipulate the data outliers, data patterns and/or data events.

Database system and methods for domain-tailored detection of outliers, patterns, and events in data streams

Methods and apparatus are provided for automatic combination of sub-process simulation results and heterogeneous data sources. An exemplary method comprises obtaining, for a process comprised of a sequence of a plurality of sub-processes, an identification of relevant input and output features for each sub-process; obtaining an execution map for each sub-process, wherein each execution map stores results of an execution of a given sub-process; and, in response to a user query regarding a target feature and a user-provided initial scenario: composing a probability distribution function for the target feature representing a simulation of the process based on a sequence of the execution maps, by matching input features of each execution map with features from the initial scenario or the output of previous execution maps; and processing the probability distribution function to answer the user query. Execution maps are optionally stored as distributed tables that use relevant input features to hash data related to multiple executions across multiple nodes. The composition process optionally occurs in parallel across multiple nodes.

Automatic combination of sub-process simulation results and heterogeneous data sources

Methods and apparatus are provided for performing massively parallel processing (MPP) large-scale combinations of time series data. A given working compute node in a distributed computing environment obtains a given group of time series data of a plurality of groups of time series data; generates a measurement matrix for the given group based on a plurality of selected time series and a plurality of time lags of the selected time series; processes the measurement matrix to generate a first linear model with a predefined number of first independent selected variables; assigns a score to each first independent selected variable; and provides the first independent selected variables and assigned scores to a master compute node that ranks the first independent selected variables for all groups from all working computing nodes according to assigned scores; selects a predefined number of second independent selected variables based on a final rank to create a final group of time series; and processes the final group of time series to generate a final linear model.

Massively parallel processing (MPP) large-scale combination of time series data

Distributed content-aware compression and decompression techniques are provided for data, such as seismic data. Data is compressed by obtaining a file chunk of a plurality of file chunks of a larger file, a start offset of the file chunk, and a data descriptor indicating how the file chunk should be processed based on characteristics of the larger file. Headers in the file chunk are compressed using a substantially lossless header compression technique to obtain compressed headers. Samples in the file chunk are compressed using a substantially lossless sample compression technique to obtain compressed samples. The compressed headers and compressed samples are packed into a single bit stream comprising a compressed version of the file chunk. The compression can be performed in a physical or logical layer of storage nodes of a file system or in compute nodes of a computing cluster. The compression can be executed on demand by an external agent and/or in a background mode by a storage operating system.

Distribution content-aware compression and decompression of data

This disclosure is directed to a touchless interactive environment. An input device may be configured to capture electronic images corresponding to physical objects detectable within a physical three-dimensional region. A computer system may establish a virtual three-dimensional region mapped to the physical three-dimensional region, with the virtual three-dimensional region defining a space where a plurality of virtual objects are instantiated based on the plurality of electronic images. The computer system may select a virtual object from the plurality of virtual objects as one or more commanding objects, with the one or more commanding objects indicating a command of a graphical user interface to be performed based on a position of the one or more commanding objects. The computer system may then perform the command of the graphical user interface based on the position of the one or more commanding objects.

Creating a virtual environment for touchless interaction

Methods and apparatus are provided for rational compression of numbers. One or more numbers are compressed by obtaining a plurality of numbers (e.g., integer numbers and/or floating point numbers), wherein each of the plurality of numbers is represented using zero or more bits to indicate an exponent at a given base of the given number and has a length representing a number of significant bits of the given number; for each of the plurality of numbers, obtaining a pair of values comprised of the length and the exponent and counting occurrences of the pair; generating a Huffman encoding comprising at least one Huffman tree based on the counting of the occurrences; generating one or more encoding maps comprising an array mapping each of the pair to a corresponding Huffman prefix code; and encoding at least one of the plurality of numbers using the one or more encoding maps.

Methods and apparatus for rational compression and decompression of numbers

Methods and apparatus are provided for rational compression of numbers. One or more numbers are compressed by obtaining a plurality of numbers (e.g., integer numbers and/or floating point numbers), wherein each of the plurality of numbers is represented using zero or more bits to indicate an exponent at a given base of the given number and has a length representing a number of significant bits of the given number; for each of the plurality of numbers, computing the length and a difference between the length and the exponent and counting occurrences of the length given the occurrence of the difference; generating a Huffman encoding comprising at least one Huffman tree based on the counting of the occurrences; generating one or more encoding maps comprising an array mapping each of the difference and the length to a corresponding Huffman prefix code; and encoding at least one of the plurality of numbers using the one or more encoding maps.

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Rio de Janeiro, Brazil

André De Almeida Maximo