Abstract: One example method includes receiving a digital image of a needle core prostate biopsy, displaying, using a display device, a magnified portion of the digital image, obtaining, from a deep learning model, Gleason scores corresponding to patches of the magnified portion of the digital image, and displaying, using the display device, a superimposed overlay on the magnified portion of the digital image based on the Gleason scores and corresponding confidence values of the Gleason scores, the superimposed overlay comprising one or more outlines corresponding one or more Gleason scores associated with the magnified portion of the digital image and comprising image patches having colors based on a Gleason score of the Gleason scores corresponding to an underlying portion of the magnified portion of the digital image and a confidence value of the corresponding Gleason score.

Abstract: In one example, a method includes generating, responsive to receiving a request for a recommendation for a group of users and based on first privacy level data for users of the group, an original list of recommendations for the group. In this example, the method further includes evaluating, by respective computational assistants associated with the users of the group and based on respective second privacy level data for the users of the group, recommendations from the original list of recommendations for inclusion in a pruned list of recommendations for the group, wherein the second privacy level is more restricted than the first privacy level. In this example, the method further includes, in response to the pruned list of recommendations including at least one recommendation, outputting, for presentation to the users of the group, the pruned list of recommendations.

Abstract: Aspects of the disclosure provide for a method for updating business information. A business at a business location and a reference image for the business at the business location may be selected for an update. From a number of more recent images, a comparison image may be selected based on a likelihood that the comparison image captures the business location. Text and visual features reference image may be compared with text and visual features of the comparison image to determine a text similarity score and a features similarity score. A confidence level indicating whether the business in the reference image is in the comparison image may then be determined using the text similarity score and the feature similarity score. According to the confidence level, the business information of the business may be updated.

Abstract: In one example, a method includes generating, responsive to receiving a request for a recommendation for a group of users and based on first privacy level data for users of the group, an original list of recommendations for the group. In this example, the method further includes evaluating, by respective computational assistants associated with the users of the group and based on respective second privacy level data for the users of the group, recommendations from the original list of recommendations for inclusion in a pruned list of recommendations for the group, wherein the second privacy level is more restricted than the first privacy level. In this example, the method further includes, in response to the pruned list of recommendations including at least one recommendation, outputting, for presentation to the users of the group, the pruned list of recommendations.

Abstract: In one example, a method includes generating, responsive to receiving a request for a recommendation for a group of users and based on first privacy level data for users of the group, an original list of recommendations for the group. In this example, the method further includes evaluating, by respective computational assistants associated with the users of the group and based on respective second privacy level data for the users of the group, recommendations from the original list of recommendations for inclusion in a pruned list of recommendations for the group, wherein the second privacy level is more restricted than the first privacy level. In this example, the method further includes, in response to the pruned list of recommendations including at least one recommendation, outputting, for presentation to the users of the group, the pruned list of recommendations.

Abstract: Aspects of the disclosure provide for a method for updating business information. A business at a business location and a reference image for the business at the business location may be selected for an update. From a number of more recent images, a comparison image may be selected based on a likelihood that the comparison image captures the business location. Text and visual features reference image may be compared with text and visual features of the comparison image to determine a text similarity score and a features similarity score. A confidence level indicating whether the business in the reference image is in the comparison image may then be determined using the text similarity score and the feature similarity score. According to the confidence level, the business information of the business may be updated.

Abstract: In one example, a method includes generating, responsive to receiving a request for a recommendation for a group of users and based on first privacy level data for users of the group, an original list of recommendations for the group. In this example, the method further includes evaluating, by respective computational assistants associated with the users of the group and based on respective second privacy level data for the users of the group, recommendations from the original list of recommendations for inclusion in a pruned list of recommendations for the group, wherein the second privacy level is more restricted than the first privacy level. In this example, the method further includes, in response to the pruned list of recommendations including at least one recommendation, outputting, for presentation to the users of the group, the pruned list of recommendations.

Abstract: Systems and methods of identifying entities are disclosed. In particular, one or more images that depict an entity can be identified from a plurality of images. One or more candidate entity profiles can be determined from an entity directory based at least in part on the one or more images that depict the entity. The one or more images that depict the entity and the one or more candidate entity profiles can be provided as input to a machine learning model. One or more outputs of the machine learning model can be generated. Each output can include a match score associated with an image that depicts the entity and at least one candidate entity profile. The entity directory can be updated based at least in part on the one or more generated outputs of the machine learning model.

Abstract: Systems and methods of identifying entities are disclosed. In particular, one or more images that depict an entity can be identified from a plurality of images. One or more candidate entity profiles can be determined from an entity directory based at least in part on the one or more images that depict the entity. The one or more images that depict the entity and the one or more candidate entity profiles can be provided as input to a machine learning model. One or more outputs of the machine learning model can be generated. Each output can include a match score associated with an image that depicts the entity and at least one candidate entity profile. The entity directory can be updated based at least in part on the one or more generated outputs of the machine learning model.

Abstract: Computer-implemented methods and systems for automatically classifying businesses from imagery can include providing one or more images of a location entity as input to a statistical model that can be applied to each image. A plurality of classification labels for the location entity in the one or more images can be generated and provided as an output of the statistical model. The plurality of classification labels can be generated by selecting from an ontology that identifies predetermined relationships between location entities and categories associated with corresponding classification labels at multiple levels of granularity. Confidence scores for the plurality of classification labels can be generated to indicate a likelihood level that each generated classification label is accurate for its corresponding location entity. Associations based on the classification labels generated for each image can be stored in a database and used to help retrieve relevant business information requested by a user.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: Aspects of the present disclosure relate to a method includes training a deep neural network using training images and data identifying one or more business storefront locations in the training images. The deep neural network outputs tight bounding boxes on each image. At the deep neural network, a first image may be received. The first image may be evaluated using the deep neural network. Bounding boxes may then be generated identifying business storefront locations in the first image.

Abstract: A method for reconstructing a three-dimension image includes receiving a plurality of two-dimensional images and projection information of the two-dimensional images, projecting a plurality of rays onto the plurality of two-dimensional images, determining correspondence information between pixels of different ones of the plurality of two-dimensional images, determining a value of each of the pixels, and reconstructing a three-dimension image by integrating the plurality of rays, wherein a position on each ray can be associated to one pixel of the plurality of two-dimensional images.

Abstract: A machine-implemented method for generating a social annotation is provided. The method includes receiving, at an electronic device, data from a passive sensor, wherein the data comprises an internet link associated with a remote server. The method also includes determining a geographical location of the electronic device, and transmitting the determined geographical location of the electronic device to the remote server associated with the internet link, so as to generate a social annotation for a physical object associated with the determined geographical location.

Abstract: Panoramic video acquisition guidance is automatically provided as feedback during panoramic video capture. Commensurate with receiving a panoramic video being captured, at least one panoramic video acquisition guidance indicator is provided. This panoramic video guidance acquisition indicator(s) provides real-time acquisition feedback on the panoramic video being captured, and is based, at least in part, on the panoramic video being captured or movement of a video capture device capturing the panoramic video. In one or more implementations, the panoramic video guidance indicator may include a video rotation progress indicator, a video tilt or video pitch indicator, and/or a video rotation speed indicator.

Abstract: Systems and methods for updating geographic data based on a transaction are provided. In some aspects, one or more transaction records associated with a business are accessed from a memory. Each transaction record identifies a transaction time, geographic location data, and transaction information. A geocoded record of the business is selected to update, based on the geographic location data of the one or more transaction records. The selected geocoded record is updated based on at least one of the transaction time or the transaction information identified in the transaction records.

Abstract: A method for three-dimensional esophageal reconstruction includes acquiring a first X-ray image from a first angle with respect to a subject using a first X-ray imager. At least a second X-ray image is acquired from a second angle, different than the first angle, with respect to the subject using a second X-ray imager. Additional X-ray images may be acquired from additional angle. A three-dimensional model of the esophagus is generated from the at least two X-ray images acquired at different angles. A set of fluoroscopic X-ray images is acquired using either the first X-ray imager or the second X-ray imager. The three-dimensional model of the esophagus is registered to the acquired set of fluoroscopic X-ray images. The three-dimensional model of the esophagus is displayed overlaying the set of fluoroscopic X-ray images.

Abstract: A method for reconstructing a three-dimension image includes receiving a plurality of two-dimensional images and projection information of the two-dimensional images, projecting a plurality of rays onto the plurality of two-dimensional images, determining correspondence information between pixels of different ones of the plurality of two-dimensional images, determining a value of each of the pixels, and reconstructing a three-dimension image by integrating the plurality of rays, wherein a position on each ray can be associated to one pixel of the plurality of two-dimensional images.

Abstract: A method for registering images of multiple modalities includes acquiring first image of a subject using a first modality. A second image of the subject is acquired using a second modality. The first image includes greater structural detail of the subject than the second image and the second image is a video image including multiple image frames. The first and second images are registered based on an anatomical structure observable in the first image and a foreign object proximate to the anatomical structure observable in the second image.

Abstract: A method for visualizing temporal phenomena and constructing 3D views from a series of medical images includes providing a first time series of digital images of contrast-enhanced blood flow in a patient, each acquired from a same viewing point with a known epipolar geometry, each said image comprising a plurality of intensities associated with an N-dimensional grid of points, calculating one or more time-density curves from said first time series of digital images, each curve indicative of how the intensity at corresponding points in successive images changes over time, and generating one or more overview images from said time density curves using a color coding technique, wherein said each overview image depict how a physical property value changes from said blood flow at selected corresponding points in said first time series of images.