Abstract: An example method of classifying user content based on a set of standards is described herein. The method includes receiving the user content, generating numeric representations of the user content, and identifying one or more standards of the set of standards applying to the user content based on a comparison of the numeric representation of the user content and numeric representations of the one or more standards.

Abstract: An item generation interface may generate knowledge assessment items directed a subject area based on a set of model items collectively directed to the subject area. The item generation interface may group the set of model assessment items into a plurality of similar item groups using numeric features corresponding to the model assessment items. Similar item groups may include model assessment items covering conceptually similar concepts within the subject area. A conditioning input may be generated for each of the item groups based on the numeric features corresponding to the model assessment items in the item group. Responsive to providing the conditioning inputs to a transformer-based natural language generation model, the item generation interface may receive raw assessment items from the transformer-based natural language generation model. Knowledge assessment items may be identified from the raw assessment items.

Abstract: A medical image processing apparatus comprises processing circuitry configured to: acquire a series of medical imaging data sets acquired across multiple time phases, wherein the medical imaging data sets are representative of a first body part and a second body part in the vicinity of the first body part, and wherein the first body part and second body part undergo relative motion across the multiple time phases; and automatically determine a viewing surface for rendering a series of images from the medical imaging data sets, wherein the determining of the viewing surface is based on respective positions of the first body part and second body part in each of the series of medical imaging data sets.

Abstract: A medical image processing apparatus comprises processing circuitry configured to: acquire a series of medical imaging data sets acquired across multiple time phases, wherein the medical imaging data sets are representative of a first body part and a second body part in the vicinity of the first body part, and wherein the first body part and second body part undergo relative motion across the multiple time phases; and automatically determine a viewing surface for rendering a series of images from the medical imaging data sets, wherein the determining of the viewing surface is based on respective positions of the first body part and second body part in each of the series of medical imaging data sets.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models based at least in part on the set of p-scores and the set of n-scores.

Abstract: An apparatus for processing volumetric image data to identify vessel regions having a predetermined condition, comprises a measurement unit for measuring at least one vessel parameter, a reference identification unit for obtaining data representing a branch-free vessel and identifying at least one reference section of the branch-free vessel, a calculation unit for calculating an expected value of the parameter in a further section of the branch-free vessel based on at least one measured value of the parameter in the at least one reference section, and a region identification unit for identifying a region of the vessel having the predetermined condition in dependence on both the expected value of the parameter in the further section and a measured value of the parameter in the further section.

Abstract: An apparatus for processing multi-energy image data to separate at least two types of material comprises a classification unit, wherein the classification unit is configured to obtain a classification of pixels or voxels belonging to the types of material based on a threshold which is adaptively changed in dependence on multi-energy intensity information associated with the pixels or voxels.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract: An apparatus for processing multi-energy image data to separate at least two types of material comprises a classification unit, wherein the classification unit is configured to obtain a classification of pixels or voxels belonging to the types of material based on a threshold which is adaptively changed in dependence on multi-energy intensity information associated with the pixels or voxels.

Abstract: A computer implemented method identifying calcification in a patient image data set including blood vessels.

Abstract: An apparatus for processing volumetric image data to identify vessel regions having a predetermined condition, comprises a measurement unit for measuring at least one vessel parameter, a reference identification unit for obtaining data representing a branch-free vessel and identifying at least one reference section of the branch-free vessel, a calculation unit for calculating an expected value of the parameter in a further section of the branch-free vessel based on at least one measured value of the parameter in the at least one reference section, and a region identification unit for identifying a region of the vessel having the predetermined condition in dependence on both the expected value of the parameter in the further section and a measured value of the parameter in the further section.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.

Abstract: A computer implemented method identifying calcification in a patient image data set including blood vessels.

Abstract: A computer implemented method for determining a vehicle type of a vehicle detected in an image is disclosed. An image having a detected vehicle is received. A number of vehicle models having salient feature points is projected on the detected vehicle. A first set of features derived from each of the salient feature locations of the vehicle models is compared to a second set of features derived from corresponding salient feature locations of the detected vehicle to form a set of positive match scores (p-scores) and a set of negative match scores (n-scores). The detected vehicle is classified as one of the vehicle models models based at least in part on the set of p-scores and the set of n-scores.