Abstract: The disclosed systems and methods provide a novel framework that leverages collected logged-in daily active users (LIDAU) data to drive network traffic to network resources, as well as engage these users to continue or remain engaged with the resources through personalization and customization according to their behaviors and patterns. LIDAU data, which is based on a raw data feed of information from network resources and an identified dataset determined from the raw data feed, is used as a basis to increase LIDAU for a specific user or a set of other users. The determined understanding of LIDAU, and its impact on users as well as the network resources the users are interacting with, enables the framework to personalize the resources that are anticipated as being visited by the users in advance of the users visiting them.

Abstract: An electronic message composition support system, method and architecture including machine learning and natural language processing techniques for extending message composition capability and support and to provide feedback to a user regarding an error, condition, etc., detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: Communication accounts may contain information such as account holder names, contact lists, and communication logs. Such information may be processed for generating features that may be used as corpus for a machine learning algorithm for developing classifiers of names. Specifically, names and contact names of the accounts may be arranged in to a document according to the manner in which account holders communicate with the contacts. The document may be used for generating word embedding of the names. Names prelabeled with ethnicity together with their word embedding may be used as training data for developing ethnicity classifiers based on machine learning algorithms.

Abstract: Disclosure includes an electronic message composition support system, method and architecture. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: An electronic message composition support system, method and architecture is provided. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: Communication accounts may contain information such as account holder names, contact lists, and communication logs. Such information may be processed for generating features that may be used as corpus for a machine learning algorithm for developing classifiers of names. Specifically, names and contact names of the accounts may be arranged in to a document according to the manner in which account holders communicate with the contacts. The document may be used for generating word embedding of the names. Names prelabeled with ethnicity together with their word embedding may be used as training data for developing ethnicity classifiers based on machine learning algorithms.

Abstract: Disclosure includes an electronic message composition support system, method and architecture. Techniques including machine learning and natural language processing techniques are used to extend message composition capability and support and to provide feedback to a user regarding an error, condition, etc. detected in the user's message before the user sends the message, e.g., while the user is composing the message using a messaging application's user interface.

Abstract: A method and system for automatic bone segmentation and landmark detection for joint replacement surgery is disclosed. A 3D medical image of at least a target joint region of a patient is received. A plurality bone structures are automatically segmented in the target joint region of the 3D medical image and a plurality of landmarks associated with a joint replacement surgery are automatically detected in the target joint region of the 3D medical image. The boundaries of segmented bone structures can then be interactively refined based on user inputs.

Abstract: A method and system for automatic pelvis unfolding from 3D computed tomography (CT) images is disclosed. A 3D medical image, such as a 3D CT image, is received. Pelvis anatomy is segmented in the 3D medical image. The 3D medical image is reformatted to visualize an unfolded pelvis based on the segmented pelvis anatomy.

Abstract: Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.

Abstract: Methods and systems for automatic classification of images of internal structures of human and animal bodies. A method includes receiving a magnetic resonance (MR) image testing model and determining a testing volume of the testing model that includes areas of the testing model to be classified as bone or cartilage. The method includes modifying the testing model so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model and producing an initial classification of the testing volume by fitting the testing volume to the mean shape and the shape variation space. The method includes producing a refined classification of the testing volume into bone areas and cartilage areas by refining the boundaries of the testing volume with respect to the active shape model and segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas.

Abstract: A method and system for automatic pelvis unfolding from 3D computed tomography (CT) images is disclosed. A 3D medical image, such as a 3D CT image, is received. Pelvis anatomy is segmented in the 3D medical image. The 3D medical image is reformatted to visualize an unfolded pelvis based on the segmented pelvis anatomy.

Abstract: Described herein is a framework for multi-view matching of regions of interest in images. According to one aspect, a processor receives first and second digitized images, as well as at least one CAD finding corresponding to a detected region of interest in the first image. The processor determines at least one candidate location in the second image that matches the CAD finding in the first image. The matching is performed based on local appearance features extracted for the CAD finding and the candidate location. In accordance with another aspect, the processor receives digitized training images representative of at least first and second views of one or more regions of interest. Feature selection is performed based on the training images to select a subset of relevant local appearance features to represent instances in the first and second views. A distance metric is then learned based on the subset of local appearance features. The distance metric may be used to perform matching of the regions of interest.

Abstract: Methods and systems for automatic classification of images of internal structures of human and animal bodies. A method includes receiving a magnetic resonance (MR) image testing model and determining a testing volume of the testing model that includes areas of the testing model to be classified as bone or cartilage. The method includes modifying the testing model so that the testing volume corresponds to a mean shape and a shape variation space of an active shape model and producing an initial classification of the testing volume by fitting the testing volume to the mean shape and the shape variation space. The method includes producing a refined classification of the testing volume into bone areas and cartilage areas by refining the boundaries of the testing volume with respect to the active shape model and segmenting the MR image testing model into different areas corresponding to bone areas and cartilage areas.

Abstract: A method and system for automatic bone segmentation and landmark detection for joint replacement surgery is disclosed. A 3D medical image of at least a target joint region of a patient is received. A plurality bone structures are automatically segmented in the target joint region of the 3D medical image and a plurality of landmarks associated with a joint replacement surgery are automatically detected in the target joint region of the 3D medical image. The boundaries of segmented bone structures can then be interactively refined based on user inputs.

Abstract: Multiple object segmentation is performed for three-dimensional computed tomography. The adjacent objects are individually segmented. Overlapping regions or locations designated as belonging to both objects may be identified. Confidence maps for the individual segmentations are used to label the locations of the overlap as belonging to one or the other object, not both. This re-segmentation is applied for the overlapping local, and not other locations. Confidence maps in re-segmentation and application just to overlap locations may be used independently of each other or in combination.

Abstract: Described herein is a framework for multi-view matching of regions of interest in images. According to one aspect, a processor receives first and second digitized images, as well as at least one CAD finding corresponding to a detected region of interest in the first image. The processor determines at least one candidate location in the second image that matches the CAD finding in the first image. The matching is performed based on local appearance features extracted for the CAD finding and the candidate location. In accordance with another aspect, the processor receives digitized training images representative of at least first and second views of one or more regions of interest. Feature selection is performed based on the training images to select a subset of relevant local appearance features to represent instances in the first and second views. A distance metric is then learned based on the subset of local appearance features. The distance metric may be used to perform matching of the regions of interest.