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 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: 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.