Abstract: A computer implemented method and system for identifying and visualizing features correlated with cancer subtypes, includes the steps of receiving a set of medical images representing an area including a patient's lesion, applying a classification model trained on lesions with known pathology by mathematically combining the model's learned weights with said medical images to produce a score indicating the likelihood of the lesion being of each particular cancer subtype, extracting importance maps from said classification model indicating features correlated with each cancer subtype by tracing the output of the model through the learned weights to identify regions of the input with a strong influence on the classification score for each cancer subtype, and displaying said importance maps visually on a computing device.

Abstract: The present invention relates to computer implemented methods and system for determining relevance measures for computational resources based on their relatedness to a user's interests. The methods and systems are designed to accept as inputs a collection of unstructured textual data related to resources, and a structured graph of the relationships between resources, to calculate probability distributions of resources over latent communities discovered from the unstructured textual data, to activate the structured graph with these probability distributions, and to spread this activation throughout the graph in a fixed number of iterations. The result of these methods and of the systems implementing these methods is a set of relevance measures attached to the resources in the structured graph.

Abstract: The present invention relates to computer implemented methods and system for determining relevance measures for computational resources based on their relatedness to a user's interests. The methods and systems are designed to accept as inputs a collection of unstructured textual data related to resources, and a structured graph of the relationships between resources, to calculate probability distributions of resources over latent communities discovered from the unstructured textual data, to activate the structured graph with these probability distributions, and to spread this activation throughout the graph in a fixed number of iterations. The result of these methods and of the systems implementing these methods is a set of relevance measures attached to the resources in the structured graph.