Abstract: Systems and methods relate to predicting disease progression by processing digital pathology images using neural networks. A digital pathology image that depicts a specimen stained with one or more stains is accessed. The specimen may have been collected from a subject. A set of patches are defined for the digital pathology image. Each patch of the set of patches depicts a portion of the digital pathology image. For each patch of the set of patches and using an attention-score neural network, an attention score is generated. The attention-score neural network may have been trained using a loss function that penalized attention-score variability across patches in training digital pathology images labeled to indicate no or low subsequent disease progression. Using a result-prediction neural network and the attention scores, a result is generated that represents a prediction of whether or an extent to which a disease of the subject will progress.

Abstract: A parking assist system for a vehicle includes an environment information obtainer, a storage, a parked vehicle detector, a space determiner, and a parking possibility determiner. The storage stores a reference parking width to be used for parking the vehicle. The parked vehicle detector detects a parked vehicle. The space determiner compares a width of a space next to the parked vehicle with the reference parking width and determines whether the width of the space is greater than or equal to the reference parking width. If the width of the space is found to be greater than or equal to the reference parking width, the parking possibility determiner determines whether a target object is detected at a back side of the space. If the target object is not detected, the parking possibility determiner determines that parking in the space is prohibited.

Abstract: A parking assist system for a vehicle includes an environment information obtainer, a sufficient parking space setter, a parked-vehicle inter-space calculator, a parking capable space setter, and a parking assister. The environment information obtainer obtains environment information around the vehicle. The sufficient parking space setter sets a sufficient parking space for the vehicle by including a parking allowance width on each of left and right sides of the vehicle. The parked-vehicle inter-space calculator calculates a space between parked vehicles, based on the obtained environment information. The parking capable space setter compares the space between the parked vehicles with the sufficient parking space and, when determining the space between the parked vehicles is greater than or equal to the sufficient parking space, sets the space between the parked vehicles to be a parking capable space. The parking assister guides the vehicle to the parking capable space.

Abstract: Embodiments disclosed herein generally relate to identifying auto-fluorescent artifacts in a multiplexed immunofluorescent image. Particularly, aspects of the present disclosure are directed to accessing a multiplexed immunofluorescent image of a slice of specimen, wherein the multiplexed immunofluorescent image comprises one or more auto-fluorescent artifacts, processing the multiplexed immunofluorescent image using a machine-learning model, wherein an output of the processing corresponds to a prediction that the multiplexed immunofluorescent image includes one or more auto-fluorescent artifacts at one or more particular portions of the multiplexed immunofluorescent image, adjusting subsequent image processing based on the prediction, performing the subsequent image processing, and outputting a result of the subsequent image processing, wherein the result corresponds to a predicted characterization of the specimen.

Abstract: The present disclosure relates to techniques for transforming digital pathology images obtained by different slide scanners into a common format for image analysis. Particularly, aspects of the present disclosure are directed to obtaining a source image of a biological specimen, the source image is generated from a first type of scanner, inputting into a generator model a randomly generated noise vector and a latent feature vector from the source image as input data, generating, by the generator model, a new image based on the input data, inputting into a discriminator model the new image, generating, by the discriminator model, a probability for the new image being authentic or fake, determining whether the new image is authentic or fake based on the generated probability, and outputting the new image when the image is authentic.

Abstract: The present invention provides a method of fraud detection in telecommunication including 4 steps: collecting user-profiling data, analyzing encrypted text messages, extracting features, building fraudulent subscribers classification model, developing keyword rule to detect fraudulent subscribers, proposing multiple options to prevent fraudulent subscribers in realtime.

Abstract: Described herein are computer-implemented methods for analysis of a tissue sample. An example method includes: annotating the whole tumor regions or set of tumorous sub-regions either on a biomarker image or an H&E image (e.g. from an adjacent serial section of the biomarker image); registering at least a portion of the biomarker image to the H&E image; detecting different cellular and regional tissue structures within the registered H&E image; computing a probability map based on the different detected structures within the registered H&E image; deriving nuclear metrics from each of the biomarker and H&E images; deriving probability metrics from the probability map; and classifying tumor nuclei in the biomarker image based on the computed nuclear and probability metrics.

Abstract: A computer-implemented method comprising: obtaining repair estimate data that describes a repair estimate for a damaged vehicle; extracting, from an OEM repair procedure database according to the received repair estimate data, repair procedure data that describes repair procedures for the damaged vehicle; transmitting the extracted repair procedure data to at least one technician computing device; obtaining repair log data generated by the at least one technician computing device, wherein the repair log data describes one or more repairs performed upon the damaged vehicle; determining whether the repair procedures for the damaged vehicle have been satisfactorily performed based on the received repair log data and the extracted repair procedure data; sending an electronic approval message responsive to determining the repair procedures for the damaged vehicle have been satisfactorily performed; and sending an electronic disapproval message responsive to determining the repair procedures for the damaged vehicle

Abstract: A parking assist system for a vehicle includes an environment information obtainer, a sufficient parking space setter, a parked-vehicle inter-space calculator, a parking capable space setter, and a parking assister. The environment information obtainer obtains environment information around the vehicle. The sufficient parking space setter sets a sufficient parking space for the vehicle by including a parking allowance width on each of left and right sides of the vehicle. The parked-vehicle inter-space calculator calculates a space between parked vehicles, based on the obtained environment information. The parking capable space setter compares the space between the parked vehicles with the sufficient parking space and, when determining the space between the parked vehicles is greater than or equal to the sufficient parking space, sets the space between the parked vehicles to be a parking capable space. The parking assister guides the vehicle to the parking capable space.

Abstract: A parking assist system for a vehicle includes the following elements. A parking space width calculator is configured to calculate a parking space width between objects. A parking width comparator is configured to compare the parking space width with the width of a virtual parking region. A movement trace detector is configured to detect a movement trace within the virtual parking region. A target detector is configured to determine whether a target faces at least one side of the virtual parking region other than the side near the vehicle. A parking priority setter is configured to set a parking priority for the virtual parking region based on a movement trace and an object. A display selector is configured to display parking space candidates based on parking priority and instructs a driver to select a parking space candidate.

Abstract: A parking assist system for a vehicle includes an environment information obtainer, a storage, a parked vehicle detector, a space determiner, and a parking possibility determiner. The storage stores a reference parking width to be used for parking the vehicle. The parked vehicle detector detects a parked vehicle. The space determiner compares a width of a space next to the parked vehicle with the reference parking width and determines whether the width of the space is greater than or equal to the reference parking width. If the width of the space is found to be greater than or equal to the reference parking width, the parking possibility determiner determines whether a target object is detected at a back side of the space. If the target object is not detected, the parking possibility determiner determines that parking in the space is prohibited.

Abstract: Described herein are systems, methods, and programming for analyzing and classifying digital pathology images. Some embodiments include receiving whole slide images (WSIs) and dividing each of the WSIs into tiles. For each WSI, a random subset of the tiles may be selected and augmented views of each of the selected tiles may be generated. For each of the selected tiles, a first convolutional neural network (CNN) may be trained to: generate, using a first one of the augmented views corresponding to the selected tile, a first representation of the selected tile, and predict a second representation of the selected tile to be generated by a second CNN, wherein the second representation is generated based on a second one of the augmented views of the selected tile.

Abstract: A driving assistance device for a vehicle includes a traveling environment recognizer, a parking space detector, an evaluation value calculator, and a parking space setter. The traveling environment recognizer is configured to recognize traveling environment information related to an outside of the vehicle. The parking space detector is configured to, in a case where the vehicle has entered a parking lot, detect one or more parking spaces available to the vehicle based on the traveling environment information. The evaluation value calculator is configured to calculate one or more evaluation values for the one or more parking spaces, each of the one or more evaluation values including an exit hindrance factor as an evaluation item. The parking space setter is configured to select, with priority, a parking space for parking the vehicle from among the one or more parking spaces. The evaluation value of the parking space is relatively high.

Abstract: The present disclosure relates machine learning techniques for segmenting non-tumor regions in specimen images to support tumor detection and analysis. Particularly, aspects of the present disclosure are directed to accessing one or more images that comprise a non-target region (e.g., a non-tumor region) and a target region (e.g., a tumor region), predicting, by a two-dimensional segmentation model, segmentation maps for the non-target region based on discriminative features encoded from the one or more images, a segmentation mask for the one or more images based on the segmentation maps, applying the segmentation mask to the one or more images to generate non-target region masked images that exclude the non-target region from the one or more images, and classifying, by an image analysis model, a biological material or structure within the target region based on a set of features extracted from the non-target region masked images.

Abstract: Systems and methods discussed herein include, among other things, a method comprising quantifying analyte staining of a biological compartment in a region in which said staining is intermixed with analyte staining of an analytically-distinct distinct biological compartment. Disclosed systems and methods include, for example, a system and method for identifying membrane staining of an analyte of interest in regions where diffuse membrane staining is intermixed with cytoplasmic staining and/or punctate staining is disclosed. Disclosed systems and methods include, for example, a system and method for quantifying membrane staining of an analyte of interest in tissue or cytological samples having regions in which membrane staining is intermixed with cytoplasmic staining and/or punctate staining.

Abstract: An image analysis system and method classify cells in a tissue image. The system and method may extract at least one image feature characterizing an object in the tissue image. Based on the extracted image feature, cells may be classified according to at least one predefined membrane pattern. For each classified cell, a class label that identifies a class to which the classified cell belongs may be outputted.

Abstract: An image analysis system and method to generate selective stain segmentation images for at least one cell type of interest within a stained tissue image are presented. Cell membranes in the tissue image are detected to generate a corresponding membrane mask image. Cell nuclei are classified to generate a classified cells image. Selective stain segmentation images are generated for the at least one cell type of interest based on the membrane mask image and the classified cells image.

Abstract: A system and a method for unveiling poorly visible or lightly colored nuclei in an input image are disclosed. An input image is fed to a color deconvolution module for deconvolution into two color channels that are processed separately before being combined. The input image is deconvolved into two separate images: a stain image and a counter stain image. A complement of the stain image is generated in order to clearly reflect the locations of the poorly visible or light-colored nuclei. The complement image and the counter stain image are optionally normalized and then combined and segmented, to generate an output image with clearly defined nuclei. Alternatively, the complement of the stain image and the counter stain image can optionally be normalized, and then segmented prior to being combined to generate the output image.

Abstract: Methods, apparatus, and processor-readable storage media for identifying and determining the criticality of enterprise assets using network traffic information are provided herein. An example computer-implemented method includes capturing network session information from an enterprise network; identifying multiple assets within the enterprise network by processing the captured network session information; determining, for each of the identified assets, one or more predefined features of the asset based at least in part on the processing of the captured network session information; determining, for each of the identified assets, a level of criticality associated with the asset based at least in part on the one or more determined features of the asset; and outputting the level of criticality and an identifier of the asset associated therewith to a security-related system, wherein the level of criticality and the asset identifier are used by the security-related system to take at least one automated action.

Abstract: Described herein are computer-implemented methods for analysis of a tissue sample. An example method includes: annotating the whole tumor regions or set of tumorous sub-regions either on a biomarker image or an H&E image (e.g. from an adjacent serial section of the biomarker image); registering at least a portion of the biomarker image to the H&E image; detecting different cellular and regional tissue structures within the registered H&E image; computing a probability map based on the different detected structures within the registered H&E image; deriving nuclear metrics from each of the biomarker and H&E images; deriving probability metrics from the probability map; and classifying tumor nuclei in the biomarker image based on the computed nuclear and probability metrics.