Methods and systems for training computer-aided condition detection systems. One method includes receiving a plurality of images for a plurality of patients, some of the images including an annotation associated with a condition; iteratively applying a first deep learning network to each of the images to produce a segmentation map, a feature map, and an image-level probability of the condition for each of the images; iteratively applying a second deep learning network to each feature map produced by the first network to produce a plurality of outputs; training the first network based on the segmentation map produced for each image; and training the second network based on the output produced for each of the patients. The second network includes a plurality of convolution layers and a plurality of convolutional long short-term memory (LSTM) layers. Each of the outputs includes a patient-level probability of the condition for one of the patients.

Methods and computer program products for training a neural network perform multiple forms of data augmentation on sample waveforms of a training dataset that includes both normal and abnormal samples to generate normal data augmentation samples and abnormal data augmentation samples. The normal data augmentation samples are labeled according to a type of data augmentation that was performed on each respective normal data augmentation sample. The abnormal data augmentation samples are labeled according to a type of data augmentation other than that which was performed on each respective abnormal data augmentation sample. A neural network model is trained to identify a form of data augmentation that has been performed on a waveform using the normal data augmentation samples and the abnormal data augmentation samples.

The invention relates to a method for lane-changing prediction of a target vehicle, the method including: receiving a velocity and a position of the target vehicle; respectively obtaining, based on the velocity and the position of the target vehicle, a first lane-changing probability and a second lane-changing probability of the target vehicle by using a first machine learning model and a second machine learning model; and determining a possibility of lane changing of the target vehicle based on the first lane-changing probability and the second lane-changing probability, the first machine learning model and the second machine learning model being pre-trained and being different from each other. The invention further relates to a device for lane-changing prediction of a target vehicle, a computer storage medium, and a vehicle.

Computer-implemented methods for detecting objects within digital image data based on color transitions include: receiving or capturing a digital image depicting an object; sampling color information from a first plurality of pixels of the digital image, wherein each of the first plurality of pixels is located in a background region of the digital image; optionally sampling color information from a second plurality of pixels of the digital image, wherein each of the second plurality of pixels is located in a foreground region of the digital image; assigning each pixel a label of either foreground or background using an adaptive label learning process; binarizing the digital image based on the labels assigned to each pixel; detecting contour(s) within the binarized digital image; and defining edge(s) of the object based on the detected contour(s). Corresponding systems and computer program products configured to perform the inventive methods are also described.

Embodiments for implementing enhanced ensemble model diversity and learning by a processor. One or more data sets may be created by combining one or more clusters of data points of a minority class with selected data points of a majority class. One or more ensemble models may be created from the one or more data sets using a supervised machine learning operation. An occurrence of an event may be predicted using the one or more ensemble models.

A computer aided method for analyzing fibrosis is provided. First, a segmentation algorithm is performed on a medical image to obtain a segmentation image. Circular fibrosis is detected according to the segmentation image to determine a score. In some cases, it is also necessary to determine a number of fibrosis bridges and the condition of fiber expansion.

The present disclosure relates to processing operations that execute image classification training for domain-specific traffic, where training operations are entirely compliant with data privacy regulations and policies. Image classification model training, as described herein, is configured to classify meaningful image categories in domain-specific scenarios where there is unknown data traffic and strict data compliance requirements that result in privacy-limited image data sets. Iterative image classification training satisfies data compliance requirements through a combination of online image classification training and offline image classification training. This results in tuned image recognition classifiers that have improved accuracy and efficiency over general image recognition classifiers when working with domain-specific data traffic. One or more image recognition classifiers are independently trained and tuned to detect an image class for image classification. Training of independent image recognition classifiers is also utilized for training and tuning of deeper learning models for image classification.

A method for performing automatic visual inspection includes: capturing visual information of an object using a scanning system including a plurality of cameras; extracting, by a computing system including a processor and memory, one or more feature maps from the visual information using one or more feature extractors; classifying, by the computing system, the object by supplying the one or more feature maps to a complex classifier to compute a classification of the object, the complex classifier including: a plurality of simple classifiers, each simple classifier of the plurality of simple classifiers being configured to compute outputs representing a characteristic of the object; and one or more logical operators configured to combine the outputs of the simple classifiers to compute the classification of the object; and outputting, by the computing system, the classification of the object as a result of the automatic visual inspection.

Systems, apparatuses and methods may store a plurality of classes that represent a plurality of clusters in a cache. Each of the classes represents a group of the plurality of clusters and the plurality of clusters is in a first data format. The systems, apparatuses and methods further modify input data from a second data format to the first data format and conduct a similarity search based on the input data in the first data format to assign the input data to at least one class of the classes.

Disclosed in the embodiments of the present invention are a method and an apparatus for training an item coding model. The method comprises: acquiring an initial item coding model and a training sample set; using sample user information of training samples in the training sample set as the input for the initial item coding model to obtain the probability of sample item coding information corresponding to the inputted sample user information; adjusting the structural parameters of the initial item coding model to train an item coding model, the item coding model being used for characterizing the correspondence between inputted sample user information and sample item coding information and the correspondence between sample item information and sample item coding information. The present embodiment can use the trained item coding model to implement item recommendation and can use the item coding information as an index to increase retrieval efficiency.