Abstract: The present disclosure provides systems and methods for predicting a disease state of a subject using ultrasound imaging and ancillary information to the ultrasound imaging. At least two quantitative measurements of a subject, including at least one measurement taken using ultrasound imaging, as part of quantified information can be identified. One of the quantitative measurements can be compared to a first predetermined standard, included as part of ancillary information to the quantified information, in order to identify a first initial value. Further, another of the quantitative measurements can be compared to a second predetermined standard, included as part of the ancillary information, in order to identify a second initial value. Subsequently, the quantitative information can be correlated with the ancillary information using the first initial value and the second initial value to determine a final value that is predictive of a disease state of the subject.

Abstract: An example system includes a processor to receive a set of features, a set of relations between the features, and a set of target features. Each of the target features is associated with a number of the relations. The processor can generate a hypergraph based on the features and the relations. The processor also can select a subset of features based on a transitive closure of the hypergraph for each of the target features. The processor can transmit the selected subset of features.

Abstract: An embodiment generates an initial set of training data from monitoring data. The initial set of training data is generated by combining outputs from a plurality of pretrained classifiers. The embodiment trains a new classification model using the initial set of training data to identify anomalies in monitoring data. The embodiment performs a multiple-level clustering of the data samples resulting in a plurality of clusters of sub-clusters of data samples, and generates a review list of data samples by selecting a representative data sample from each of the clusters. The embodiment receives an updated data sample from the expert review that includes a revised target classification for at least one of the data samples of the expert review list. The embodiment then trains another replacement classification model using a revised set of training data that includes the updated data sample and associated revised target classification.

Abstract: A computing device, method and computer program product are provided to generate synthetic images of abnormalities on the surface of an object, such as a vehicle. The synthetic images of abnormalities on the surface of an object may be utilized for training a machine learning algorithm to detect and/or classify abnormalities. In the context of a method, a respective abnormality is parametrically modeled by selecting one or more control points that satisfy parameters associated with the respective abnormality and generating a surface representative of the respective abnormality based on the one or more control points. The method also renders a synthetic image of at least a portion of the surface of the object having the respective abnormality as defined by the parametric modeling thereof. The rendering of the synthetic image includes rendering the synthetic image in accordance with a predefined lighting condition and from a predefined viewpoint.

Abstract: An image encoder is provided including circuitry and a memory coupled to the circuitry. The circuitry, in operation, responds to a size of a block satisfying a size condition by generating a prediction image using a prediction mode selected from a plurality of prediction modes. The plurality of prediction modes include a first prediction mode in which a prediction process uses a motion vector and a reference block in a same picture as the block. The circuitry encodes the block using the prediction image.

Abstract: Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for improved image segmentation using hyperspectral imaging. In some implementations, a system obtains image data of a hyperspectral image, the image data comprising image data for each of multiple wavelength bands. The system accesses stored segmentation profile data for a particular object type that indicates a predetermined subset of the wavelength bands designated for segmenting different region types for images of an object of the particular object type. The system segments the image data into multiple regions using the predetermined subset of the wavelength bands specified in the stored segmentation profile data to segment the different region types. The system provides output data indicating the multiple regions and the respective region types of the multiple regions.

Abstract: A processor performs machine learning, which uses a radiographic image that does not include a suture needle as a surgical tool and a surgical tool image that includes the suture needle in a posture different from a linear posture as training data, to construct a trained model for detecting a region of the suture needle from an input radiographic image.

Abstract: An image signal processing method includes: detecting a high frequency component in an input image received from an image sensor; calculating a ratio of the detected high frequency component; reconstructing a high frequency image based on the calculated ratio; and outputting an output image by combining the reconstructed high frequency image with a non-high frequency image of the input image.

Abstract: A deep neural network (DNN) can be trained based on a first training dataset that includes first images including annotated first objects. The DNN can be tested based on the first training dataset to determine first object predictions including first uncertainties. The DNN can be tested by inputting a second training dataset and outputting first object predictions including second uncertainties, wherein the second training dataset includes second images including unannotated second objects. A subset of images included in the second training dataset can be selected based on the second uncertainties, The second objects in the selected subset of images included in the second training dataset can be annotated. The DNN can be trained based on the selected subset of images included in the second training dataset including the annotated second objects.

Abstract: A method and apparatus for pedestrian re-identification, an electronic device, and a computer-readable storage medium are provided. The method includes that: a pedestrian image to be detected is acquired; global feature information of the pedestrian image to be detected is extracted through multiple convolutional layers of a convolutional neural network; multiple pieces of intermediate feature information of the pedestrian image to be detected are extracted through the multiple convolutional layers of the convolutional neural network respectively, and the multiple pieces of intermediate feature information are merged as local feature information; and the global feature information and the local feature information are assigned as a classification feature of the pedestrian image to be detected, and a classification result of the pedestrian image to be detected is determined according to the classification feature.

Abstract: Devices, systems, and methods obtain first radiographic-image data reconstructed based on a set of projection data acquired in a radiographic scan; apply one or more trained machine-learning models to the set of projection data and the first radiographic-image data to obtain a set of parameters for a scatter kernel; input the set of parameters and the set of projection data into the scatter kernel to obtain scatter-distribution data; and perform scatter correction on the set of projection data using the scatter-distribution data, to obtain a set of corrected projection data.

Abstract: An image decoding method according to the present document comprises the steps of: deriving transform coefficients through inverse quantization on the basis of quantized transform coefficients for a target block; deriving modified transform coefficients on the basis of an inverse reduced secondary transform (RST) for the transform coefficients; and generating, on the basis of an inverse primary transform for the modified transform coefficients, a restoration picture based on residual samples for the target block, wherein the modified transform coefficients derived according to the inverse RST are two-dimensionally arranged according to the order of a row priority direction or a column priority direction according to an intra prediction mode to be applied to the target block.

Abstract: A method of manufacturing a patient interface for sealed delivery of a flow of air at a continuously positive pressure with respect to ambient air pressure to an entrance to the patient's airways includes collecting anthropometric data of a patient's face. Anticipated considerations are identified from the collected anthropometric data during use of the patient interface. The collected anthropometric data is processed to provide a transformed data set based on the anticipated considerations, the transformed data set corresponding to at least one customised patient interface component. At least one patient interface component is modelled based on the transformed data set.

Abstract: Apparatus for binning an input value into an array of bins, each bin representing a range of input values and the bins collectively representing a histogram of input values, the apparatus comprising: an input for receiving the input value; a memory for storing the array; and a binning controller configured to: derive a plurality of bin values from the input value according to a binning distribution located about the input value, the binning distribution spanning a range of input values and each bin value having a respective input value dependent on the position of the bin value in the binning distribution; and allocate the plurality of bin values to a plurality of bins in the array, each bin value being allocated to a bin selected according to the respective input value of the bin value.

Abstract: Embodiments of the present disclosure relate to a method, an electronic device, and a computer program product for training a data classification model. The method includes generating a first training rule based on probabilities of classifying a plurality of sample data into corresponding classes by a data classification model. The method also includes generating a second training rule based on relevances of the plurality of sample data to the corresponding classes. In addition, the method also includes training the data classification model using the first training rule and the second training rule. With this method, a data classification model is trained, so that the data classification accuracy of the data classification model and the robustness to noise can be improved.

Abstract: Disclosed are a system and method for managing a self-contained hydrogen power system for a smart farm. The system may include a self-contained hydrogen generation unit configured to purify intake-water, generate clean hydrogen through water electrolysis, generate energy through a fuel cell by using the generated clean hydrogen, and store the energy, and a farm environment control unit configured to receive, from the self-contained hydrogen generation unit, energy for driving a plurality of sensors and devices for producing aquatic products and control an environment for producing aquatic products.

Abstract: A machine-learning classification system includes a first machine-learning classifier that classifies each element of a plurality of data items to generate a plurality of classified data items. A second machine-learning classifier identifies misclassified elements of the plurality of classified data items and reclassifies each of the identified misclassified elements to generate a plurality of reclassified data items. A second machine-learning classifier identifies unclassified elements of the plurality of classified data items and classifies each of the identified unclassified elements to generate a plurality of reclassified data items. An ensemble classifier adjusts the classifications of the elements of the plurality of classified data items in response to the plurality of reclassified data items and the plurality of newly-classified elements.

Abstract: Aspects of the present disclosure pertain to systems and methods for enhancing brightfield or darkfield images to better enable nucleus detection. In some embodiments, the systems and methods described herein are useful for identifying membrane stain biomarkers as well as nuclear/cytoplasm stain biomarkers in stained images of biological samples. In some embodiments, the presently disclosed systems and methods enable quick and accurate nucleus detection in stained images of biological samples, especially for original stained images of biological samples where the nuclei appear faint.

Abstract: Provided is a method for performing accurate object recognition in a stable manner in consideration of changes in a shooting environment. In such a method, a camera captures an image of a shooting location where an object is to be placed and an object included in an image of the shooting location is recognized utilizing a machine learning model for object recognition. The method further involves: determining necessity of an update operation on the machine learning model for object recognition at a predetermined time; when the update operation is necessary, causing the camera to capture an image of the shooting location where no object is placed to thereby re-acquire a background image for training; and causing the machine learning model to be trained using a composite image of a backgroundless object image and the re-acquired background image for training as training data.

Abstract: Techniques are generally described for machine learning exampled-based annotation of image data. In some examples, a first machine learning model may receive a query image comprising a first depiction of an object-of-interest. In some examples, the first machine learning model may receive a target image representing a scene in which a second depiction of the object-of-interest is visually represented. In various examples, the first machine learning model may generate annotated output image data that identifies a location of the second depiction of the object-of-interest within the target image. In some examples, an object detection model may be trained based at least in part on the annotated output image data.