A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of video features in the digital video data, analyzing the plurality of video features to create a relationship graph, determining a relationship score based on the relationship graph using a first computer-implemented machine learning model, and outputting the relationship score with a user interface. The interacting event comprises a plurality of interactions between a first individual and a second individual and each video feature of the plurality of video features corresponds to an interaction of the plurality of interactions. The relationship graph comprises a first node, a second node, and a first edge extending from the first node to the second node. The first node represents the first individual, the second node represents the second individual, and a weight of the first edge represents a relationship strength between the first individual and the second individual.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data, and analyzing the plurality of features to create a relationship graph. The relationship graph comprises a plurality of nodes and a plurality of edges, each node of the plurality of nodes represents an individual of the plurality of individuals, and each edge of the plurality of edges extends between two nodes of the plurality of nodes, and the plurality of edges represents a plurality of interactions of the interacting event. The method further includes identifying an edge of the plurality of edges as an anomalous edge, creating an output representative of the anomalous edge, and outputting the output representative of the anomalous edge. The anomalous edge is identified by a computer-implemented machine learning model configured to identify anomalous edges in relationship graphs.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data with a first computer-implemented machine learning model, analyzing the plurality of features to create a baseline relationship graph, determining a target relationship graph, generating one or more actions for increasing similarity between the baseline relationship graph and the target relationship graph, and outputting the one or more actions by a user interface. The one or more actions are generated using a simulator, a second computer-implemented machine learning model, and a plurality of actions. The second computer-implemented machine learning model is configured to relate actions of the plurality of actions to changes to relationship graphs, the simulator is configured to simulate changes to the baseline relationship graph using the second computer-implemented machine learning model and the plurality of actions.

A method includes acquiring digital video data that portrays an interacting event, identifying a plurality of features in the digital video data with a first computer-implemented machine learning model, analyzing the plurality of features to create a baseline relationship graph, determining a target relationship graph, generating one or more actions for increasing similarity between the baseline relationship graph and the target relationship graph, and outputting the one or more actions by a user interface. The one or more actions are generated using a simulator, a second computer-implemented machine learning model, and a plurality of actions. The second computer-implemented machine learning model is configured to relate actions of the plurality of actions to changes to relationship graphs, the simulator is configured to simulate changes to the baseline relationship graph using the second computer-implemented machine learning model and the plurality of actions.

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

Methods, systems, articles of manufacture, and apparatus to recalibrate confidences for image classification are disclosed. An example apparatus to classify an image includes an image crop detector to detect a first image crop from the image, the first image crop corresponding to a first object, a grouping controller to select a second image crop corresponding to a second object at a location of the first object, a prediction generator to, in response to executing a trained model, determine a label corresponding to the first object and a confidence level associated with the label, and a confidence recalibrator to recalibrate the confidence level based on a probability of the first object having a first attribute based on the second object having a second attribute, the confidence level recalibrated to increase an accuracy of the image classification.

Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method rely on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.

Graph-theoretic segmentation methods for segmenting histological structures in H&E stained images of tissues. The method rely on characterizing local spatial statistics in the images. Also, a method for quantifying intratumor spatial heterogeneity that can work with single biomarker, multiplexed, or hyperplexed immunofluorescence (IF) data. The method is holistic in its approach, using both the expression and spatial information of an entire tumor tissue section and/or spot in a TMA to characterize spatial associations. The method generates a two-dimensional heterogeneity map to explicitly elucidate spatial associations of both major and minor sub-populations.

Disclosed is system and method of connection information regularization, graph feature extraction and graph classification based on adjacency matrix. By concentrating the connection information elements in the adjacency matrix into a specific diagonal region of the adjacency matrix in order to reduce the non-connection information elements in advance. The subgraph structure of the graph is further extracted along the diagonal direction using the filter matrix. Then a stacked convolutional neural network is used to extract a larger subgraph structure. On the one hand, it greatly reduces the amount of computation and complexity, solving the limitations of the computational complexity and the limitations of window size. And on the other hand, it can capture large subgraph structure through a small window, as well as deep features from the implicit correlation structures at both vertex and edge level, which improves the accuracy and speed of the graph classification.