Abstract: This invention provides a system and method for analyzing and classifying imaged from whole slides of tissue. A source of image data transmits images of the tissue on the whole slides to a GPU. The GPU performs a feature extraction process that identifies and segments regions of interests in each of the images, and an attention network that, based upon training from an expert, identifies trained characteristics can comprise cancerous and/or pre-cancerous conditions/e.g. those associated with a gastrointestinal tract, such ad Barret's Esophagus. The feature extraction process can include a convolutional neural network (CNN). The attention network can be adapted performs attention/based weighting of features relative to the trained characteristics, and/or the attention network can include 3D convolutional filters. The image data is acquired using an image sensor having approximately 100 Megapixel resolution.

Abstract: This invention provides systems and methods that can detect incidental OVFs in CT examinations at the level of practicing radiologists. The OVF detection system leverages a deep convolutional neural network (CNN) to extract radiological features from each CT scan slice. These extracted features are processed through a feature aggregation module to make the final diagnosis for the full CT scan. Feature aggregation, can be performed in a variety of ways, including the use of a long short-term memory (LSTM) network. In one example, the CNN can be trained on a predetermined number of CT scans, which are established as reference standards. This result can effectively the performance of practicing radiologists on this test set in real world clinical circumstances. The system and method can be employed to assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists.

Abstract: Architecture comprising a framework for direct answer triggering, a step-by-step answer, and the user interface that facilitates the architecture. Generally, the architecture can comprise an answer datastore that stores an answer index of answer data, and a triggering component configured to invoke answer data in response to processing of a query that triggers a direct answer in a search results page. The triggering component can be configured to send an answer triggering message to the answer datastore and receive the answer data based on the answer triggering message.

Abstract: Systems, methods, and computer-readable storage media for detecting shifts in intent for search queries are provided. The system includes databases and servers. The databases store search logs and entity mappings. The servers merge the entity mappings with search logs, identify shifts in intent for recurring queries in the search log, identify intents for new queries in the search log, and updates mappings between an entity and a query based on the shifted intents. The server may provide client devices that display a search box where queries are entered. The search box may include an autosuggest area that is updated to include spiking entities or spiking queries.