A medical information processing apparatus comprises an obtaining unit that obtains medical information, a learning unit that performs learning on a function of the medical information processing apparatus using the medical information, an evaluation data holding unit that holds evaluation data in which a correct answer to be obtained by executing the function is known, the evaluation data being for evaluating a learning result of the learning unit, an evaluating unit that evaluates a learning result obtained through learning, based on the evaluation data, and an accepting unit that accepts an instruction to apply a learning result of the learning unit to the function.

Certain aspects of the present disclosure provide techniques for automatically detecting and classifying tumor regions in a tissue slide. The method generally includes obtaining a digitized tissue slide from a tissue slide database and determining, based on output from a tissue classification module, a type of tissue of shown in the digitized tissue slide. The method further includes determining, based on output from a tumor classification model for the type of tissue, a region of interest (ROI) of the digitized tissue slide and generating a classified slide showing the ROI of the digitized tissue slide and an estimated diameter of the ROI. The method further includes displaying on an image display unit, the classified slide and user interface (UI) elements enabling a pathologist to enter input related to the classified slide.

Apparatuses, systems, and techniques to identify orientations of objects within images. In at least one embodiment, one or more neural networks are trained to identify an orientations of one or more objects based, at least in part, on one or more characteristics of the object other than the object's orientation.

A system to detect a feature in an input image comprising a processor to evaluate a model including: four layers including: a supragranular layer, a granular layer, a first infragranular layer, and a second infragranular layer, each of the layers including a base connection structure including: an excitatory layer including a excitatory neurons arranged in a two dimensional grid; and an inhibitory layer including a inhibitory neurons arranged in a two dimensional grid; within-layer connections between the neurons of each layer in accordance with a Gaussian distribution; between-layer connections between the neurons of different layers, the probability of a neuron of a first layer of the different layers to a neuron of a second layer of the different layers in accordance with a uniform distribution; and input connections from lateral geniculate nucleus (LGN) neurons of an input LGN layer to the granular layer in accordance with a uniform distribution.

A gamified application is provided for users to feed animated virtual characters with images of real-world food items. The images fed to the virtual characters are to be uploaded to a data store in a cloud environment, for use in training a custom machine learning model. In one embodiment, a server in a cloud environment receives a photo of a food item fed to a virtual character in an augmented reality environment in the gamified application executing on a user device, invokes the custom machine learning model or a set of image recognition APIs to generate classification information for the photo, sends the classification information to the user device for verification by a user, and stores the verified information to the data store used for periodically training the machine learning model. Over time, the data store would include a large volume of food images with label data verified by a large number of users.

A computer-implemented method is disclosed. The method includes a) accessing a first image, b) accessing a second image, c) from an adversarial pattern generating system, generating a face recognition adversarial pattern for display from a specified region of a face corresponding to the second image, the face recognition adversarial pattern operable to minimize a measure of distance as determined by a face recognition system, between the face and a class of the first image, or to maximize a probability of the misclassification of the second image by the face recognition system, d) providing a face mountable device, that is mounted on the face, access to the face recognition adversarial pattern in real time via a communications component, and e) controlling light patterns on the face mountable device according to the face recognition adversarial pattern.

The invention relates to method and system for classifying faces of a Boundary Representation (B-Rep) model using Artificial Intelligence (AI). The method includes extracting topological information corresponding to each of a plurality of data points of a B-Rep model of a product; determining a set of parameters based on the topological information corresponding to each of the plurality of data points; transforming the set of parameters corresponding to each of the plurality of data points of the B-Rep model into a tabular format to obtain a parametric data table; and assigning each of the plurality of faces of the B-Rep model a category from a plurality of categories based on the parametric data table using an AI model.

An information processing device is provided that includes an operation control unit which controls the operations of an autonomous mobile object that performs an action according to a recognition operation. Based on the detection of the start of teaching related to pattern recognition learning, the operation control unit instructs the autonomous mobile object to obtain information regarding the learning target that is to be learnt in a corresponding manner to a taught label. Moreover, an information processing method is provided that is implemented in a processor and that includes controlling the operations of an autonomous mobile object which performs an action according to a recognition operation. Based on the detection of the start of teaching related to pattern recognition learning, the controlling of the operations includes instructing the autonomous mobile object to obtain information regarding the learning target that is to be learnt in a corresponding manner to a taught label.

A learning assistance device acquires a plurality of learned discriminators obtained by causing learning discriminators provided in a plurality of respective terminal devices to perform learning using image correct answer data, acquires a plurality of discrimination results obtained by causing a plurality of learned discriminators to discriminate the same input image, determines the correct answer data of the input image on the basis of the plurality of discrimination results, causes the discriminator to perform learning the input image and the correct answer data, and outputs a result thereof as a new learning discriminator to each terminal device.

An embodiment provides a system and method for sample labeling. During operation, the system obtains a plurality of historical loss assessment images and obtains a plurality of candidate samples from the plurality of loss assessment images. A respective candidate sample comprises an image of a candidate damage area detected in a corresponding historical loss assessment image. The system clusters the plurality of candidate samples into a plurality of class clusters. For a respective class cluster, the system determines a center candidate sample set corresponding to a class cluster center of the respective class cluster, receives a manual labeling result associated with candidate samples in the determined center candidate sample set, and performs, according to the manual labeling result, damage classification labeling on other unlabeled candidate samples in the respective class cluster to obtain a plurality of labeled samples.