Abstract: Methods and systems for identifying levels of an ophthalmic disease are described. An example method includes generating, by a convolutional neural network (CNN) and using a 3D image of a retina, a vector. The method further includes generating, by a first model and using the vector, a first likelihood that the retina exhibits a first level of an disease and generating, by a second model and using the vector, a second likelihood that the retina exhibits a second level of the disease. The method further includes determining whether the retina exhibits an absence of the ophthalmic disease, the first level of the disease, or the second level of the disease based on the first likelihood and the second likelihood. Further, an indication of whether the retina exhibits the absence of the ophthalmic disease, the first level of the ophthalmic disease, or the second level of the ophthalmic disease is output.

Abstract: Methods and systems for generating biomarker activation maps (BAMs) are described. An example method includes identifying a medical image depicting at least a portion of a subject; generating a BAM by inputting the medical image into a trained, U-shaped neural network (NN); and outputting the BAM overlaying the medical image, the BAM indicating at least one biomarker depicted in the medical image that is indicative of a disease.