Abstract: A method for aligning two different magnified images of the same subject includes a first step of precomputing rigid transformations for the two different images globally (i.e., for all or approximately all regions of the images). Pairs of corresponding features in the two different magnified images are identified and transformation values are assigned to each of the pairs of corresponding features. In a second step, while images are being generated for display to a user, a locally optimal rigid transformation for the current field of view is computed. In a third step the images are aligned as per the locally optimal rigid transformation. Non-zero weight is given to transformation values for pairs of features that are outside the current field of view. Typically, the second and third steps are repeated many times as the images are generated for display and user either changes magnification or pans/navigates to a different location in the images.

Abstract: A method is disclosed of processing a set of images. Each image in the set has an associated counterpart image. One or more regions of interest (ROIs) are identified in one or more of the images in the set of images. For ROI identified, a reference region is identified in the associated counterpart image. ROIs and associated reference regions are cropped out, thereby forming cropped pairs of images 1 . . . n1, that are fed to a deep learning model trained to make a prediction of probability of a state of the ROI, e.g., disease state, which generates a prediction Pi-, (i=1 . . . n) for each cropped pair. The model generates an overall prediction P from each of the predictions Pi. A visualization of the set of medical images and the associated counterpart images including the cropped pair of images is generated.

Abstract: A method for aligning two different magnified images of the same subject includes a first step of precomputing rigid transformations for the two different images globally (i.e., for all or approximately all regions of the images). Pairs of corresponding features in the two different magnified images are identified and transformation values are assigned to each of the pairs of corresponding features. In a second step, while images are being generated for display to a user, a locally optimal rigid transformation for the current field of view is computed. In a third step the images are aligned as per the locally optimal rigid transformation. Non-zero weight is given to transformation values for pairs of features that are outside the current field of view. Typically, the second and third steps are repeated many times as the images are generated for display and user either changes magnification or pans/navigates to a different location in the images.