A biological image presentation device includes: an acquisition unit; a determination unit determining an image as a standard and an image for comparison; an extraction unit extracting, from the image as a standard, a position where a change of luminance value equal to or larger than a defined value is present; a detection unit detecting a position corresponding to the position extracted from the image for comparison; a division unit dividing the image as a standard on the basis of the position extracted; a mapping unit mapping the image for comparison to an area corresponding to each divided area of the image as a standard while modifying so as to conform to the shape of the divided area; and a display control unit switching and displaying an image for display in a display area by using the image as a standard and an image mapped by the mapping unit.

Embodiments facilitate prediction of anti-vascular endothelial growth (anti-VEGF) therapy response in DME patients. A first set of embodiments discussed herein relates to training of a machine learning classifier to determine a prediction for response to anti-VEGF therapy based on a set of graph-network features and a set of morphological features generated based on FA images of tissue demonstrating DME. A second set of embodiments discussed herein relates to determination of a prediction of response to anti-VEGF therapy for a DME patient (e.g., non-rebounder vs. rebounder, response vs. non-response) based on a set of graph-network features and a set of morphological features generated based on FA imagery of the patient.

This disclosure relates to improved techniques for performing image segmentation functions using neural network architectures. The neural network architecture can include an attentive graph neural network (AGNN) that facilitates performance of unsupervised video object segmentation (UVOS) functions and image object co-segmentation (IOCS) functions. The AGNN can generate a graph that utilizes nodes to represent images (e.g., video frames) and edges to represent relations between the images. A message passing function can propagate messages among the nodes to capture high-order relationship information among the images, thus providing a more global view of the video or image content. The high-order relationship information can be utilized to more accurately perform UVOS and/or IOCS functions.

Biometric health monitoring of a specific user or population is performed during biometric authentication for granting access to physical or digital assets. If biometric authentication, biometric verification and biometric health monitoring is acceptable, access to the physical or digital assets is allowed. Likewise, if a health anomaly is detected in a specific user or if an outbreak is detected in a specific community, an electronic notification can be sent to the individual, a health administrator, or to a government official, and access may be denied to the specific user.

An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

An image analysis method and an image analysis system are disclosed. The method may include extracting training raw graphic data including at least one first node corresponding to a plurality of histological features of a training tissue slide image, and at least one first edge defined by a relationship between the histological features and generating training graphic data by sampling the first node of the training raw graphic data. The method may also include determining a parameter of a readout function by training a graph neural network (GNN) using the training graphic data and training output data corresponding to the training graphic data, and extracting inference graphic data including at least one second node corresponding to a plurality of histological features of an inference tissue slide image, and at least one second edge decided by a relationship between the histological features of the inference tissue slide image.

A computing system for adaptive point generation includes a storage to store a densely sampled polyline or surface, or mathematical function, and a processor to compute the area of a contour of the polyline or function with respect to itself, or compute the volume of the surface or function with respect to itself, adaptively resample the polyline, surface, or function, wherein the adaptive resampling is based on and inversely proportional to the computed area or volume, and connect adaptively resampled points as an adaptively sampled polyline or surface.

Computer-implemented methods are provided for generating a data structure representing tabular information in a scanned image. Such a method can include storing image data representing a scanned image of a table, processing the image data to identify positions of characters and lines in the image, and mapping locations in the image of information cells, each containing a set of the characters, in dependence on said positions. The method can also include, for each cell, determining cell attribute values, dependent on the cell locations, for a predefined set of cell attributes, and supplying the attribute values as inputs to a machine-learning model trained to pre-classify cells as header cells or data cells in dependence on cell attribute values.

A method for tracking a lane on a road is presented. The method comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further includes generating, by the one or more processors, a predicted spline comprising (i) a first spline and (ii) a predicted extension of the first spline in a direction in which the imaging system is moving. The first spline describes a boundary of a lane and is generated based on the set of pixels. The predicted extension of the first spline is generated based at least in part on a curvature of at least a portion of the first spline.

A method for detecting boundaries of lanes on a road is presented. The method comprises receiving, by one or more processors from an imaging system, a set of pixels associated with lane markings. The method further includes partitioning, by the one or more processors, the set of pixels into a plurality of groups. Each of the plurality of groups is associated with one or more control points. The method further includes generating, by the one or more processors, a spline that traverses the control points of the plurality of groups. The spline traversing the control points describes a boundary of a lane.