Abstract: An image processor includes an image generator configured to generate corresponding image data corresponding to first microscopic image data obtained under a first observation condition, based on second microscopic image data and third microscopic image data obtained under a second observation condition, and an image output unit configured to output the corresponding image data. The corresponding image data may be image data corresponding to a first focal plane from which the first microscopic image data are obtained, and wherein the second microscopic image data and the third microscopic image data may be image data on a second focal plane and a third focal plane, respectively, which are different from the first focal plane.

Abstract: Techniques for classifying a human cutaneous tissue specimen are presented. The techniques may include obtaining a computer readable image of the human tissue sample and preprocessing the image. The techniques may include applying a trained deep learning model to the image to label each of a plurality of image pixels with at least one probability representing a particular diagnosis, such that a labeled plurality of image pixels is obtained. The techniques can also include applying a trained discriminative classifier to contiguous regions of pixels defined at least in part by the labeled plurality of image pixels to obtain a specimen level diagnosis, where the specimen level diagnosis includes at least one of: basal cell carcinoma, dermal nevus, or seborrheic keratosis. The techniques can include outputting the specimen level diagnosis.

Abstract: A system, method, and device for generating Pepper's Ghost artifices from two-dimensional sources including Augmented Reality (“AR”) or Virtual Reality (“VR”) environments such that the Pepper's Ghost artifices appear to be a natural addition to a three-dimensional virtual environment. By employing virtual Pepper's Ghost projections onto varying size and shaped screens in the virtual three-dimensional environment, a reduction in bandwidth and costs are realized particularly when displaying humanoid characters in an AR and/or VR environment. While the methods disclosed with the present invention could be of utility to any type of virtual environment, the benefits are particularly acute for dynamic VR environments.

Abstract: Methods and systems disclosed herein may quantify a representation of a type of input an image analysis system should expect. The image analysis system may be trained on the type of input the image analysis system should expect using a first image stream. A first model of the type of input that the image analysis system should expect may be built from the first image stream. After the first model is built, a second image, or a second image stream, may be compared to the first model to determine a difference between the second image, or second image stream, and the first image stream. When the difference is greater than or equal to a threshold, a drift may be detected and steps may be taken to determine the cause of the drift.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: Systems and methods for determining a location based on image data are provided. A method can include receiving, by a computing system, a query image depicting a surrounding environment of a vehicle. The query image can be input into a machine-learned image embedding model and a machine-learned feature extraction model to obtain a query embedding and a query feature representation, respectively. The method can include identifying a subset of candidate embeddings that have embeddings similar to the query embedding. The method can include obtaining a respective feature representation for each image associated with the subset of candidate embeddings. The method can include determining a set of relative displacements between each image associated with the subset of candidate embeddings and the query image and determining a localized state of a vehicle based at least in part on the set of relative displacements.

Abstract: There is provided a parcel recognition device that recognizes a parcel based on an image including the parcel, the device including: a processor; and a memory, in which by cooperating with the memory, the processor recognizes a person in a recognition target space in the image, subtracts a space within a predetermined range including at least a part of the recognized person from the recognition target space, and recognizes the parcel by using a space obtained by the subtraction as a new recognition target space.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: Implementations of the present specification disclose a data identification method, apparatus, device, and a computer-readable medium. A solution includes: obtaining a first data set, data samples in the first data set being at least a part of data of a to-be-identified field; obtaining a state transition matrix set generated based on statistics of data samples in a second data set, a data type of the data samples in the second data set being known; determining sample state transition probabilities corresponding to the data samples in the first data set based on the state transition matrix set; determining a ratio between a number of data samples in the first data set whose sample state transition probabilities are greater than a first threshold and a total number of the data samples in the first data set; and determining data corresponding to the to-be-identified field as being of a same data type as the data samples in the second data set in response to that the ratio is greater than a second threshold.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: An information processing apparatus detects an object queue from a video frame and generates tracking information indicating a position of each tracking target object, where each object included in the object queue is the tracking target object. The information processing apparatus generates queue behavior information related to a behavior of the object queue at a first time point using the tracking information at the first time point. The information processing apparatus computes an estimated position of each tracking target object at a second time point later than the first time point based on the tracking information and the queue behavior information at the first time point. The information processing apparatus updates the tracking information based on the position of each object detected from the video frame at the second time point and the estimated position of each tracking target object at the second time point.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: Provided is a technique for extracting information with which it is possible to track an object to be tracked, even if it happens that the object to be tracked is hidden or the like. This image processing device is provided with: a moving region identification unit which identifies, in a video, the image region associated with a moving object shown in the video; a stationary region identification unit which identifies, in the video, the image region associated with a stationary object shown in the video; and an extraction unit which extracts a feature of a partial image that is included in the image region associated with the stationary object, and that does not overlap the image region associated with the moving object.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and/or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and/or dynamically identify one or more features, such as plaque and vessels, and/or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and/or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and/or quantified parameters.

Abstract: Each of a plurality of edge computing devices are configured to receive data streams generated by at least one sensor forming part of a respective medical device (e.g., a sensor-equipped surgical tool, etc.) which, in turn, characterizes use of the respective medical device in relation to a particular patient. Each of the edge computing devices can execute at least one machine learning model which generates or from which model attributes are derived. The generated model attributes are anonymized using an anonymization technique such as k-anonymity. The anonymized generated model attributes are homomorphically encrypted and transmitted to a central server. Encrypted model attribute updates to at least one of the machine learning models are later received from the central server which results in the machine learning models executing on one or more of the edge computing devices to be updated based on the received encrypted model attribute updates.

Abstract: A method and system for remote diagnosis of a cognitive disorder in humans are provided. The system comprises receive, over a network, at least one facial image of a patient; extracting, from the at least one facial image, at least one facial feature indicative of a cognitive decline; classifying the at least one extracted facial feature using a classifier, wherein the classifier maps a plurality of candidate facial features to a score indicating a stage of a cognitive decline; and determining a positive diagnosis of a cognitive decline of the patient, based on the score provided by the classifier in response to the at least one extracted facial feature.

Abstract: The present disclosure is directed towards computer-based systems and methods for evaluating a user's body motion based on motion data obtained via a series of wireless sensors attached to a user's body. In one embodiment, a computer-implemented method is disclosed herein. A server system receives motion data from one or more input devices. The motion data corresponds to movement of a user. The server system generates a motion profile based on at least the motion data received from the one or more input devices. The server system retrieves a pre-defined target motion profile from a database structure. The server system objectively evaluating the extracted motion profile by comparing one or more parameters of the generated motion profile with one or more parameters of the retrieved pre-defined target motion profile.

Abstract: An information processing apparatus for determining an area of a packaged object in a packaging material to suctioned by a suction device for picking up the packaged object includes an input unit configured to input an image obtained by capturing the packaged object, and a determining unit configured to, based on a state of a surface of the packaging material regarding a degree of ease of suction in each area of the surface of the packaging material identified based on the image, determine the area to be suctioned by the suction device.