Abstract: The invention relates to a computing system and a computer-implemented method for classifying images of retina of eyes of subjects. A captured image of a retina is processed to obtain a plurality of different segmented images each having different selected portions of the captured image using different selection rules. The multiple segmented images are provided to respective dedicated machine learning models to output an image classification based on the respective segmented images provided as input. An ensemble classification is determined based on the multiple classifications obtained by means of the multiple trained machine learning models.

Abstract: A computing system configured to perform the steps of dividing both a saliency map and a ground-truth feature map into cells in order to obtain segmented saliency map and a segmented feature map, wherein a relevance score is assigned to each cell based on values of individual pixels within the cells in the saliency map and feature map, selecting, for both the segmented saliency map and segmented feature map, a selected number of selected cells corresponding to the most relevant cells having highest relevance scores within the segmented saliency map and the segmented feature map, respectively, and computing a level of agreement between the segmented saliency map and the segmented feature map by comparing the selected cells having highest relevance scores in the segmented saliency map to the selected cells having highest relevance scores in the segmented feature map.