A computer-implemented method, a computing system, and a computer program product, for automatically labeling an amount of unlabeled data for training one or more classifiers of a machine learning system. A method includes iteratively processing unlabeled data items. Receiving an unlabeled data item into each autoencoder in an autoencoder architecture. Each autoencoder processing with a lowest loss of information the unlabeled data item that is likely associated with a label associated with the autoencoder, while processing with a higher loss of information the unlabeled data item that is likely not associated with the label. Predicting, based on loss of information, a probability distribution for the unlabeled data item. Automatically associating the label to the unlabeled data item, based on the label being associated with a highest probability in a peaking probability distribution associated with the unlabeled data item. The autoencoder architecture can include a cloud computing network architecture.

The subject disclosure presents systems and computer-implemented methods for providing reliable risk stratification for early-stage cancer patients by predicting a recurrence risk of the patient and to categorize the patient into a high or low risk group. A series of slides depicting serial sections of cancerous tissue are automatically analyzed by a digital pathology system, a score for the sections is calculated, and a Cox proportional hazards regression model is used to stratify the patient into a low or high risk group. The Cox proportional hazards regression model may be used to determine a whole-slide scoring algorithm based on training data comprising survival data for a plurality of patients and their respective tissue sections. The coefficients may differ based on different types of image analysis operations applied to either whole-tumor regions or specified regions within a slide.

A method of processing x-ray images comprises training an artificial neural network to process multi-spectral x-ray projections to determine composition information about an object in terms of equivalent thickness of at least one basis material. The method further comprises providing a multi-spectral x-ray projection of an object, wherein the multi-spectral x-ray projection of the object contains energy content information describing the energy content of the multi-spectral x-ray projection. The multi-spectral x-ray projection is then processed with the artificial neural network to determine composition information about the object, and then the composition information about the object is provided.

The present disclosure discloses a sample acquisition method, a target detection model generation method, a target detection method, a computing device, and a computer readable medium. The sample acquisition method includes: adding a perturbation to a pre-marked sample original box in an original image to obtain a sample selection box, wherein an image framed by the sample original box contains a target; and extracting an image framed by the sample selection box as a sample. The technical solutions of the present disclosure can effectively increase the number of the samples that can be acquired in the original image, and adding a background to the samples can effectively improve the recognition accuracy of the trained target detection model.

A method and system for training a model for image generation. The model includes a hybrid variational auto-encoder (VAE)—generative adversarial network (GAN) framework. The method includes the steps of: multiple input of an input image into the VAE which outputs in response multiple distinct output image samples, determining the best of the multiple output image samples as a best-of-many sample, the best-of-many sample having the minimum reconstruction cost, and training the model based on a predefined training objective, the predefined training objective integrating the best-of-many sample reconstruction cost and a GAN-based synthetic likelihood term.

A method, computer system and computer-readable medium for determining a surface pattern for a target object using an evolutionary algorithm such as a genetic algorithm, a parameterized texture-generating function, a 3D renderer for rendering images of a 3D model of the target object with a texture obtained from the parameterized texture generating function, and an object recognition model to process the images and predict whether or not the image contains an object of the target object's type or category. Sets of parameters are generated using the evolutionary algorithm and the accuracy of the object recognition model's prediction of the images with the 3D model textured according to each set of parameters is used to determine a fitness score, by which sets of parameters are scored for the purpose of obtaining future further generations of sets of parameters, such as by genetic algorithm operations such as mutation and crossover operations. The surface pattern is obtained based on the images of the 3D model rendered with a surface texture generated according to a high-scoring set of parameters.

Facilitating the description or configuration of a computer vision model by generating a data structure comprising a plurality of language entities defining a semantic mapping of visual parameters to a visual parameter space based on a sensitivity analysis of the computer vision model.

An online system receives contextual information for a goal-oriented environment at a current time and generates Q-value predictions that indicate likelihoods that one or more participants will reach the desired goal. The Q-value for a current time may also be interpreted as the value of the actions taken at the current time with respect to the desired goal. The online system generates Q-value predictions for a current time by applying an approximator network to the contextual information for the current time. In one instance, the approximator network is a machine learning model neural network model trained by a reinforcement learning process. The reinforcement process allows the approximator network to incrementally update the Q-value predictions given new information throughout time, and results in a more computationally efficient training process compared to other types of supervised or unsupervised machine learning model processes.

An object recognition device 80 includes a scene determination unit 81, a learning-model selection unit 82, and an object recognition unit 83. The scene determination unit 81 determines, based on information obtained during driving of a vehicle, a scene of the vehicle. The learning-model selection unit 82 selects, in accordance with the determined scene, a learning model to be used for object recognition from two or more learning models. The object recognition unit 83 recognizes, using the selected learning model, an object in an image to be photographed during driving of the vehicle.

Techniques are provided for increasing the accuracy of automated classifications produced by a machine learning engine. Specifically, the classification produced by a machine learning engine for one photo-realistic image is adjusted based on the classifications produced by the machine learning engine for other photo-realistic images that correspond to the same portion of a 3D model that has been generated based on the photo-realistic images. Techniques are also provided for using the classifications of the photo-realistic images that were used to create a 3D model to automatically classify portions of the 3D model. The classifications assigned to the various portions of the 3D model in this manner may also be used as a factor for automatically segmenting the 3D model.