Abstract: An example system includes a processor to receive an image with corresponding acquisition information. The processor is to classify the image using the corresponding acquisition information via a deep neural network including integrated acquisition information.

Abstract: A system and a method for training machine learning models, using features predicted by other models, third party models, legacy models, and the like, for training dataset augmentation. Many datasets have many items with unknown, missing, or erroneous values of features. The method comprises using additional machine learning models to predict unknown features, optionally generate a distribution from their inferred score, and use a plurality of scores from the distribution and/or scores from further additional machine learning models, to create replicas for item with missing features, having different estimations of the unknown features. Followingly, train the machine learning model using data items with the known features and the scores for the unknown feature of the item.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for annotating magnetic resonance imaging (MRI) images. The method may include receiving mammogram (MG) image data representing annotated MG images of a patient breast, the annotated MG images being one of either a craniocaudal view or of a mediolateral oblique view. The method may include identifying annotations representing an abnormality at a first location in the annotated MG images; receiving MRI image data representing MRI images of the patient breast; generating annotated MRI image data using the MRI image data and the annotations identified in the annotated MG images, the annotated MRI image data including MRI annotations at a second location based at least in part on the first location, the MRI annotations in the annotated MRI image data representing the abnormality; and storing the annotated MRI image data in a database.

Abstract: An approach for improving determining a significant slice associated with a tumor from a volume of medical images is disclosed. The approach is based on the annotation of tumor range and the slice index in which the tumor appears to have the largest area. The approach infer a tumor growth classifier on sliding window of the volume slices and creates a discrete integral function out of the classifier predictions. The approach applies post processing on the discrete integral function which can include a smoothing function and a bias correction. The approach selects the slice index of maximum value from the post processing step.

Abstract: An example system includes a processor to evaluate a trained first classifier on a test set of labeled data to generate error rates for a number of labels. The processor is to process a set of unlabeled data via the trained first classifier to generate annotated data including labels and associated error rates. The processor is to train a second classifier using the annotated data and the associated error rates.

Abstract: An approach for improving determining a significant slice associated with a tumor from a volume of medical images is disclosed. The approach is based on the annotation of tumor range and the slice index in which the tumor appears to have the largest area. The approach infer a tumor growth classifier on sliding window of the volume slices and creates a discrete integral function out of the classifier predictions. The approach applies post processing on the discrete integral function which can include a smoothing function and a bias correction. The approach selects the slice index of maximum value from the post processing step.

Abstract: Embodiments herein disclose computer-implemented methods, computer program products and computer systems for annotating magnetic resonance imaging (MRI) images. The method may include receiving mammogram (MG) image data representing annotated MG images of a patient breast, the annotated MG images being one of either a craniocaudal view or of a mediolateral oblique view. The method may include identifying annotations representing an abnormality at a first location in the annotated MG images; receiving MRI image data representing MRI images of the patient breast; generating annotated MRI image data using the MRI image data and the annotations identified in the annotated MG images, the annotated MRI image data including MRI annotations at a second location based at least in part on the first location, the MRI annotations in the annotated MRI image data representing the abnormality; and storing the annotated MRI image data in a database.

Abstract: A method including: automatically detecting, using at least one machine learning algorithm, one or more abnormalities depicted in a medical image of a patient; automatically determining whether the one or more abnormalities have remained temporally and unchanged, based on an older medical image of the patient; and upon determining that the one or more abnormalities have remained temporally and spatially unchanged: automatically inpainting the one or more abnormalities in the medical image, and automatically enrich a new training set with the inpainted medical image.

Abstract: Embodiments may include techniques to choose a model based on a similarity of computed features of an input to computed features of several models in order to improve feature analysis using Machine Learning models. A method of image analysis may comprise extracting a training feature vector corresponding to each of the plurality of machine learning models from each validation image from a plurality of machine learning models trained using a plurality of validation images, extracting from a new image a new feature vector corresponding to each of the plurality of machine learning models, comparing each new feature vector corresponding to each machine learning model with the training feature vector corresponding to each of the plurality of machine learning models, and selecting and outputting an inference for the new image generated by the machine learning model for which the new feature vector and the training feature vector are most similar.

Abstract: A method which includes: Obtaining a training set which comprises: multiple data pairs each comprising: (i) a raw sensory signal acquired by a medical imaging system, and (ii) a processed image generated by the medical imaging system from the raw sensory signal; and a classification label for each of the data pairs. Based on the training set, training an artificial neural network (ANN), wherein the training comprises minimizing a global loss which is a weighted sum of: a loss between the classification labels and classification predictions by the ANN, and a similarity loss between the processed images and images generated by an intermediate layer of the ANN. The training is such that the trained ANN is configured, for a new raw sensory signal: to predict a new classification, and to generate a new image by the intermediate layer of the ANN.

Abstract: Embodiments may include techniques to choose a model based on a similarity of computed features of an input to computed features of several models in order to improve feature analysis using Machine Learning models. A method of image analysis may comprise extracting a training feature vector corresponding to each of the plurality of machine learning models from each validation image from a plurality of machine learning models trained using a plurality of validation images, extracting from a new image a new feature vector corresponding to each of the plurality of machine learning models, comparing each new feature vector corresponding to each machine learning model with the training feature vector corresponding to each of the plurality of machine learning models, and selecting and outputting an inference for the new image generated by the machine learning model for which the new feature vector and the training feature vector are most similar.

Abstract: An example system includes a processor to evaluate a trained first classifier on a test set of labeled data to generate error rates for a number of labels. The processor is to process a set of unlabeled data via the trained first classifier to generate annotated data including labels and associated error rates. The processor is to train a second classifier using the annotated data and the associated error rates.

Abstract: A method including: automatically detecting, using at least one machine learning algorithm, one or more abnormalities depicted in a medical image of a patient; automatically determining whether the one or more abnormalities have remained temporally and unchanged, based on an older medical image of the patient; and upon determining that the one or more abnormalities have remained temporally and spatially unchanged: automatically inpainting the one or more abnormalities in the medical image, and automatically enrich a new training set with the inpainted medical image.

Abstract: A method which includes: Obtaining a training set which comprises: multiple data pairs each comprising: (i) a raw sensory signal acquired by a medical imaging system, and (ii) a processed image generated by the medical imaging system from the raw sensory signal; and a classification label for each of the data pairs. Based on the training set, training an artificial neural network (ANN), wherein the training comprises minimizing a global loss which is a weighted sum of: a loss between the classification labels and classification predictions by the ANN, and a similarity loss between the processed images and images generated by an intermediate layer of the ANN. The training is such that the trained ANN is configured, for a new raw sensory signal: to predict a new classification, and to generate a new image by the intermediate layer of the ANN.

Abstract: An example system includes a processor to receive an image with corresponding acquisition information. The processor is to classify the image using the corresponding acquisition information via a deep neural network including integrated acquisition information.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with the principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with the principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with said principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A computer-executable application is provided with access to resources generated using a principal model. A decoration model associated with said principal model is instantiated for use by the application. The application is provided with access to an object of the decoration model responsive to a request by the application to access an object of the principal model. The decoration model object corresponds to the principal model object.

Abstract: A method for implementing a model-driven architecture, including defining a principal model having a plurality of classes, references, attributes, and associations between any of the classes, the model configured to facilitate the automatic generation of at least one resource for use by a computer-executable application, where a change to the principal model subsequent to performing the automatic generation requires the automatic generation be performed again in order to effect the change for use by the application, defining a decoration model having a class, reference, and attribute for any corresponding one of the primary model classes, references, and attributes, where a change to the decoration model subsequent to performing the automatic generation does not require the automatic generation be performed again in order to effect the change for use by the application, mapping the decoration model to the principal model, and storing both of the models on a computer-readable medium.