Abstract: A computer-aided diagnosis (CAD) system for classification and visualisation of a 3D medical image comprises a classification component comprising a 2D convolutional neural network (CNN) that is configured to generate a prediction of one or more classes for 2D slices of the 3D medical image. The system also comprises a visualisation component that is configured to: determine, for a target class of said one or more classes, which slices belong to the target class; for each identified slice, determine, by back-propagation to an intermediate layer of the CNN, a contribution of each pixel of the identified slice to classification of the identified slice as belonging to the target class; and generate a heatmap that provides a visual indication of the contributions of respective pixels.

Abstract: A method is proposed for automatically analysing a retina image, to identify the presence of drusen which is indicative of age-related macular degeneration. The method proposes dividing a region of interest including the macula centre into patches, obtaining a local descriptor of each of the patches, reducing the dimensionality of the local descriptor by comparing the local descriptor to a tree-like clustering model and obtaining transformed data indicating the identity of the cluster. The transformed data is fed into an adaptive model which generates data indicative of the presence of drusen in the retinal image. Furthermore, the trans formed data can be used to obtain the location of the drusen within the image.