Abstract: To provide automated categorization of structured textual content individual nodes of textual content, from a document object model encapsulation of the structured textual content, have a multidimensional vector associated with them, where the values of the various dimensions of the multidimensional vector are based on the textual content in the corresponding node, the visual features applied or associated with the textual content of the corresponding node, and positional information of the textual content of the corresponding node. The multidimensional vectors are input to a neighbor-imbuing neural network. The enhanced multidimensional vectors output by the neighbor-imbuing neural network are then be provided to a categorization neural network. The resulting output can be in the form of multidimensional vectors whose dimensionality is proportional to categories into which the structured textual content is to be categorized. A weighted merge takes into account multiple nodes that are grouped together.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, and matching the initial 3D model with the subsequent 3D model based on a registration.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, and matching the initial 3D model with the subsequent 3D model based on a registration.

Abstract: To provide automated categorization of structured textual content individual nodes of textual content, from a document object model encapsulation of the structured textual content, have a multidimensional vector associated with them, where the values of the various dimensions of the multidimensional vector are based on the textual content in the corresponding node, the visual features applied or associated with the textual content of the corresponding node, and positional information of the textual content of the corresponding node. The multidimensional vectors are input to a neighbor-imbuing neural network. The enhanced multidimensional vectors output by the neighbor-imbuing neural network are then be provided to a categorization neural network. The resulting output can be in the form of multidimensional vectors whose dimensionality is proportional to categories into which the structured textual content is to be categorized. A weighted merge takes into account multiple nodes that are grouped together.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, and matching the initial 3D model with the subsequent 3D model based on a registration.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, matching the initial 3D model with the subsequent 3D model based on a registration, and performing geometric filtering and/or topological filtering on the matching of the initial 3D model and the subsequent 3D model.

Abstract: Systems, methods, and computer-readable media for registering initial computed tomography (CT) images of a luminal network with subsequent CT images of the luminal network include obtaining initial CT images of the luminal network and subsequent CT images of the luminal network, generating an initial three-dimensional (3D) model of the luminal network based on the initial CT images of the luminal network, generating a subsequent 3D model of the luminal network based on the subsequent CT images of the luminal network, matching the initial 3D model with the subsequent 3D model based on a registration, and performing geometric filtering and/or topological filtering on the matching of the initial 3D model and the subsequent 3D model.