A computer-aided diagnostic (CAD) system and method for non-invasive detection of cancer includes receiving and analyzing data from a plurality of sources, using a neural network to generate an initial classification probability from each data source, assigning weights to the initial classification probabilities, and integrating the initial classification probabilities to generate a final classification. The final classification may be a designation of a tissue, such as a pulmonary nodule, as cancerous or noncancerous.

An embodiment generates a training batch of data points from training data for a plurality of classes and builds a multi-class classifier having a series of binary classifiers arranged in a first order. Each of the binary classifiers is associated with a respective class. The embodiment trains the multi-class classifier with the binary classifiers arranged in a first order and, at each binary classifier, the embodiment identifies data points as belonging to the class associated with the respective classifier and updates the training batch to exclude the classified data points. The embodiment then modifies the multi-class classifier by changing the order of classifiers and repeats the training of the multi-class classifier with the series of binary classifiers arranged in a second order. The embodiment then selects a final configuration of the multi-class classifier based at least in part on a comparison of first training results to the second training results.

The present disclosure is directed to systems and methods for performing object detection and pose estimation in 3D from 2D images. Object detection can be performed by a machine-learned model configured to determine various object properties. Implementations according to the disclosure can use these properties to estimate object pose and size.

An image processing method, for an identity document that comprises a data page, comprises acquiring a digital image of the page of data of the identity document. The method further comprises assigning a class or a super-class to the candidate identity document via automatic classification of the digital image by a machine-learning algorithm trained beforehand on a set of reference images in a training phase; processing the digital image to obtain a set of at least one intermediate image the weight of which is lower than or equal to the weight of the digital image; applying discrimination to the intermediate image using a discriminator neural network; and generating an output signal as output from the discriminator neural network, the value of which is representative of the probability that the candidate identity document is an authentic document or a fake.

An image processing method, for an identity document that comprises a data page, comprising comprises acquiring a digital image of the page of data of the identity document. The method further comprises assigning a class or a super-class to the candidate identity document via automatic classification of the digital image by a machine-learning algorithm trained beforehand on a set of reference images in a training phase; processing the digital image to obtain a set of at least one intermediate image the weight of which is lower than or equal to the weight of the digital image; applying discrimination to the intermediate image using a discriminator neural network; and generating an output signal as output from the discriminator neural network, the value of which is representative of the probability that the candidate identity document is an authentic document or a fake.

An ensemble learning based method is for a binary classification on an imbalanced dataset. The imbalanced dataset has a minority class comprising positive samples and a majority class comprising negative samples. The method includes: generatively oversampling the imbalanced dataset by synthetically generating minority class examples, thereby generating a generated dataset; using the generated dataset to generate subsamples, and learning a base classifier on each of the subsamples to determine a plurality of base classifiers; and learning a weighted majority vote classifier by combining outputs of the base classifiers. Each of the base classifiers is assigned a weight in such a way that a diversity between the base classifiers on the positive samples is minimized.

In some aspects, the disclosure is directed to methods and systems for detection and classification of stamps in documents. The system can receive image data and textual data of a document. The system can pre-process and filter that data, and covert the textual data to a term frequency inverse document frequency (TF-IDF) vector. The system can detect the presence of a stamp on the document. The system can extract a subset of the image data including the stamp. The system can extract text from the subset of the image data. The system can classify the stamp using the extracted text, the image data, and the TF-IDF vector. The system can store the classification in a database.

An automated method and system for validating (cross-validating) data fields in an electronic document, such as a document that has been passed through an optical character recognition (“OCR”) or Intelligent Document Recognition (“IDR”) system or software, to improve accuracy of the electronic document.

Methods, apparatuses, and embodiments related to analyzing the content of digital images. A computer extracts multiple sets of visual features, which can be keypoints, based on an image of a selected object. Each of the multiple sets of visual features is extracted by a different visual feature extractor. The computer further extracts a visual word count vector based on the image of the selected object. An image query is executed based on the extracted visual features and the extracted visual word count vector to identify one or more candidate template objects of which the selected object may be an instance. When multiple candidate template objects are identified, a matching algorithm compares the selected object with the candidate template objects to determine a particular candidate template of which the selected object is an instance.

Methods, apparatuses, and embodiments related to analyzing the content of digital images. A computer extracts multiple sets of visual features, which can be keypoints, based on an image of a selected object. Each of the multiple sets of visual features is extracted by a different visual feature extractor. The computer further extracts a visual word count vector based on the image of the selected object. An image query is executed based on the extracted visual features and the extracted visual word count vector to identify one or more candidate template objects of which the selected object may be an instance. When multiple candidate template objects are identified, a matching algorithm compares the selected object with the candidate template objects to determine a particular candidate template of which the selected object is an instance.