A computer-implemented method for automatically generating metadata tags for a medical image includes receiving a medical image and automatically identifying a set of body landmarks in the medical image using one or more machine learning models. A set of rules are applied to the set of body landmarks to identify anatomical objects present in the image. As an alternative to using the set of rules, in some embodiments, one or more machine learning models to the set of body landmarks to identify anatomical objects present in the image. Once the anatomical objects are identified, metadata tags corresponding to the anatomical objects are generated and stored in the medical image. Then, the medical image with the metadata tags is transferred to a data repository.

Aspects of the disclosure provide methods and apparatuses for recommending a sticker set. An apparatus for recommending a sticker set includes interface circuitry and processing circuitry. When the interface circuitry receives a sticker recommendation request from a terminal, the processing circuitry determines a historical sticker set that includes a sticker previously sent by a user of the terminal device, and at least one recommendable sticker set not including the historical sticker set. Then the processing circuitry determines a recommendation index for each of the at least one recommendable sticker set according to an emotion feature of the historical sticker set and an emotion feature of the respective recommendable sticker set. According to the recommendation index for each of the at least one recommendable sticker set, the interface circuitry sends a sticker set recommendation for one or more of the at least one recommendable sticker set to the terminal device.

The ability to automate the processes of specimen collection, image acquisition, data pre-processing, computation of derived biomarkers, modeling, classification and analysis can significantly impact clinical decision-making and fundamental investigation of cell deformation. This disclosure combine 3D cell nuclear shape modeling by robust smooth surface reconstruction and extraction of shape morphometry measure into a highly parallel pipeline workflow protocol for end-to-end morphological analysis of thousands of nuclei and nucleoli in 3D. This approach allows efficient and informative evaluation of cell shapes in the imaging data and represents a reproducible technique that can be validated, modified, and repurposed by the biomedical community. This facilitates result reproducibility, collaborative method validation, and broad knowledge dissemination.

A device may receive human-related data associated with a surveyor and a surveyed person participating in an interview, and may receive environmental data. The device may determine, based on rules, that first portions of the human-related data and environmental data are more reliable than second portions, and may process the first portions of the human-related data and the environmental data, with a first model, to determine high-reliability context data. The device may process the second portions of the human-related data and the environmental data, with a second model, to determine low-reliability context data, and may process the high-reliability context data and the low-reliability context data, with a third model, to generate weighted context data. The device may process the weighted context data, with a fourth model, to calculate a total stress factor, and may perform actions based on the total stress factor.

The technology described herein is directed to a media indexer framework including a character recognition engine that automatically detects and groups instances (or occurrences) of characters in a multi-frame animated media file. More specifically, the character recognition engine automatically detects and groups the instances (or occurrences) of the characters in the multi-frame animated media file such that each group contains images associated with a single character. The character groups are then labeled and used to train an image classification model. Once trained, the image classification model can be applied to subsequent multi-frame animated media files to automatically classifying the animated characters included therein.

Automatic document classification using machine learning may involve receiving inputs that assign documents to classifiers, which define document classification rules for a classification model. The computing device may train the classification model using a machine learning technique that assigns each document of a second set of documents to destinations based on the document classification rules. The computing device may also receive a template design for each destination that specifies metadata to extract for a document type corresponding to documents assigned to the destination. The computing device may subsequently classifying a particular document using the classification model, which may involve assigning the particular document to a given destination of the plurality of destinations based on the document classification rules, and exporting metadata from the particular document using the template design associated with the given destination.

A novel distributed method for machine learning is described, where the algorithm operates on a plurality of data silos, such that the privacy of the data in each silo is maintained. In some embodiments, the attributes of the data and the features themselves are kept private within the data silos. The method includes a distributed learning algorithm whereby a plurality of data spaces are co-populated with artificial, evenly distributed data, and then the data spaces are carved into smaller portions whereupon the number of real and artificial data points are compared. Through an iterative process, clusters having less than evenly distributed real data are discarded. A plurality of final quality control measurements are used to merge clusters that are too similar to be meaningful. These distributed quality control measures are then combined from each of the data silos to derive an overall quality control metric.

This application provides systems and methods for training a language model to perform one or more specific natural language processing tasks. The embodiments described herein fine-tune language models for downstream tasks solely by pre-processing the training data set. Rather than fine-tuning via architecture changes (e.g., addition of classification layers on top of a language model), the embodiments described herein fine-tune language model(s) via dataset pre-processing alone. This is much simpler for the practitioner. Furthermore, it allows iterative additions of functionality to the language model without a complete restructure of the architecture. This is possible because of the general nature of the language-modelling task, which essentially consists of predicting what comes next in a sequence given some context. If training data can be framed in this manner, a language model can be used to solve that task directly without architecture modifications.

A system and method is provided for using rules to perform a set of actions on video data when conditions are satisfied by the video data. The system receives rules to select a theme, portions of the video data and/or a type of output. For example, based on annotation data associated with the video data, the system may apply rules to select one or more themes, with each of theme associated with a portion of the video data. In some examples, the system may apply rules to determine the portion of the video data associated with the theme. The system may apply rules to generate various types of output data associated with each of the selected themes, the types of output data may include a video summarization, individual video clips, individual video frames, a photo album including video frames selected from the video data or the like.

The disclosed computer-implemented method for enabling search services to highlight documents may include (1) creating, via an internal search service, a highlight index that comprises an analyzer for at least one type of document, (2) receiving a search query configured for an external search service and a document that is of the type and that comprises a search result for the search query, (3) querying the highlight index in order to retrieve the analyzer for the type of document from the highlight index, and (4) sending the analyzer, the document, and the search query to a search service in order to enable the search service to display at least one highlighted string extracted from the document via the analyzer, wherein the highlighted string originates from the search query. Various other methods, systems, and computer-readable media are also disclosed.