A system comprising one or more computers and one or more storage devices storing instructions that when executed by the one or more computers cause the one or more computers to implement: a first set of one or more segmentation neural networks (110, 112, 114), wherein each segmentation neural network in the first set is configured to: receive an input image of eye tissue captured using a first imaging modality; and process the input image to generate a segmentation map (116, 118, 120) that segments the eye tissue in the input image into a plurality of tissue types; a set of one or more classification neural networks (132, 134, 136), wherein each classification neural network is configured to: receive a classification input (126, 128, 130) comprising a segmentation map of eye tissue; and process the classification input to generate a classification output (138, 140, 142) that characterizes the eye tissue; and a subsystem configured to: receive a first image of eye tissue captured using the first imaging modality; provide the first image as input to each of the segmentation neural networks in the first set to obtain one or more segmentation maps of the eye tissue in the first image; generate, from each of the segmentation maps, a respective classification input; and provide, for each of the segmentation maps, the classification input for the segmentation map as input to each of the classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network; and generate, from the respective classification outputs for each of the segmentation maps, a final classification output for the first image; wherein the input image of eye tissue captured using the first imaging modality is a three-dimensional image comprising a plurality of voxels, and wherein the segmentation map assigns a respective tissue type, or tissue type probability, from a predetermined set of tissue types to each of the voxels. a first set of one or more segmentation neural networks (110, 112, 114), wherein each segmentation neural network in the first set is configured to: receive an input image of eye tissue captured using a first imaging modality; and process the input image to generate a segmentation map (116, 118, 120) that segments the eye tissue in the input image into a plurality of tissue types; receive an input image of eye tissue captured using a first imaging modality; and process the input image to generate a segmentation map (116, 118, 120) that segments the eye tissue in the input image into a plurality of tissue types; a set of one or more classification neural networks (132, 134, 136), wherein each classification neural network is configured to: receive a classification input (126, 128, 130) comprising a segmentation map of eye tissue; and process the classification input to generate a classification output (138, 140, 142) that characterizes the eye tissue; and receive a classification input (126, 128, 130) comprising a segmentation map of eye tissue; and process the classification input to generate a classification output (138, 140, 142) that characterizes the eye tissue; and a subsystem configured to: receive a first image of eye tissue captured using the first imaging modality; provide the first image as input to each of the segmentation neural networks in the first set to obtain one or more segmentation maps of the eye tissue in the first image; generate, from each of the segmentation maps, a respective classification input; and provide, for each of the segmentation maps, the classification input for the segmentation map as input to each of the classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network; and generate, from the respective classification outputs for each of the segmentation maps, a final classification output for the first image; receive a first image of eye tissue captured using the first imaging modality; provide the first image as input to each of the segmentation neural networks in the first set to obtain one or more segmentation maps of the eye tissue in the first image; generate, from each of the segmentation maps, a respective classification input; and provide, for each of the segmentation maps, the classification input for the segmentation map as input to each of the classification neural networks to obtain, for each segmentation map, a respective classification output from each classification neural network; and generate, from the respective classification outputs for each of the segmentation maps, a final classification output for the first image; wherein the input image of eye tissue captured using the first imaging modality is a three-dimensional image comprising a plurality of voxels, and wherein the segmentation map assigns a respective tissue type, or tissue type probability, from a predetermined set of tissue types to each of the voxels.