Abstract: A system for facilitating text analysis is configurable to (i) receive input text data comprising a set of reference text and at least a first set of text, wherein the set of reference text and the first set of text each comprise structured components; process the input text data utilizing a syntax and verb usage module of a natural language processing (NLP) layer; generate a mapping of structured components of the first set of text to structured components of the set of reference text by processing output of the syntax and verb usage module utilizing a similarity analysis module or a categorization module of the NLP layer; and generate an output depicting one or more aspects of the mapping.

Abstract: The disclosed techniques are focused on processes for encoding an enhanced image with non-image data. Notably, the “non-image data” is distinct from “image data” in that the image data defines display characteristics of an image (e.g., display properties of a pixel) while the non-image data is unrestricted and can describe any data, even data different than display characteristics. An image is accessed, where the image includes at least one pixel that is associated with at least one color channel. Non-image data is encoded into the color channel. An index, which maps where the non-image data has been encoded in the color channel of the pixel, is generated or modified. As a result of encoding the non-image data into the color channel, an enhanced image is generated.

Abstract: A system for determining volume of a selectable region is configurable to (i) obtain user input directed to a 3D representation of a set of 2D images and (ii) based on the user input, selectively modify one or more mask pixels of one or more respective selection masks. Each 2D image of the set of 2D images is associated with a respective selection mask. The 3D representation represents pixels of the set of 2D images with corresponding voxels. The user input selects one or more voxels of the 3D representation. The one or more mask pixels is associated with one or more pixels of the set of 2D images that correspond to the one or more voxels of the 3D representation selected via the user input.

Abstract: Techniques for updating a visualization of a neural network and for refining a neural network are disclosed. Network data is obtained, where this data describes the neural network. At least some of the network data is normalized. A visual representation of the neural network is generated. The visual representation includes a set of nodes. The visual representation further includes edges connecting various nodes. The visual representation is updated using the normalized network data. As a result of updating the visual representation using the normalized network data, a display of the nodes and/or of the edges is modified in a manner to reflect a relative relationship that exists between the nodes and/or the edges. The relative relationship is based on the normalized network data. The updated visual representation is then displayed.

Abstract: Techniques are disclosed for aligning fiducial markers that commonly exist in each of multiple different N-dimensional (N-D) data sets. Notably, the N-D data sets are at least three-dimensional (3D) data sets. A first set and a second set of N-D data are accessed. A set of one or more fiducial markers that commonly exist in both those sets are identified. Based on the fiducial markers, one or more transformations are performed to align the two sets. Performing this alignment process results in at least a selected number of the common fiducial markers that exist in the two sets being within a threshold alignment relative to one another.

Abstract: Techniques are disclosed for aligning fiducial markers that commonly exist in each of multiple different N-dimensional (N-D) data sets. Notably, the N-D data sets are at least three-dimensional (3D) data sets. A first set and a second set of N-D data are accessed. A set of one or more fiducial markers that commonly exist in both those sets are identified. Based on the fiducial markers, one or more transformations are performed to align the two sets. Performing this alignment process results in at least a selected number of the common fiducial markers that exist in the two sets being within a threshold alignment relative to one another.

Abstract: The disclosed techniques are focused on processes for encoding an enhanced image with non-image data. Notably, the “non-image data” is distinct from “image data” in that the image data defines display characteristics of an image (e.g., display properties of a pixel) while the non-image data is unrestricted and can describe any data, even data different than display characteristics. An image is accessed, where the image includes at least one pixel that is associated with at least one color channel. Non-image data is encoded into the color channel. An index, which maps where the non-image data has been encoded in the color channel of the pixel, is generated or modified. As a result of encoding the non-image data into the color channel, an enhanced image is generated.