A machine learning system, comprising: at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data; and at least one processor communicably coupled to the at least one nontransitory processor-readable storage medium, in operation the at least one processor: receives learning data comprising a plurality of batches of unlabelled image sets, wherein each image set comprises a source image and target image that each represents a medical image scan of at least one patient; trains one or more convolutional neural networks (CNNs) models, based on the learning data, to learn one or more transformation functions for coregistration of a target image onto a source image, wherein the one or more CNNs models include a global network component that learns an affine transformation matrix and a local network component that learns a dense deformation field, and wherein the global network component precedes the local network component in the one or more CNNs models; and stores the one or more trained CNN models in the at least one nontransitory processor-readable storage medium of the machine learning system. at least one nontransitory processor-readable storage medium that stores at least one of processor-executable instructions or data; and at least one processor communicably coupled to the at least one nontransitory processor-readable storage medium, in operation the at least one processor: receives learning data comprising a plurality of batches of unlabelled image sets, wherein each image set comprises a source image and target image that each represents a medical image scan of at least one patient; trains one or more convolutional neural networks (CNNs) models, based on the learning data, to learn one or more transformation functions for coregistration of a target image onto a source image, wherein the one or more CNNs models include a global network component that learns an affine transformation matrix and a local network component that learns a dense deformation field, and wherein the global network component precedes the local network component in the one or more CNNs models; and stores the one or more trained CNN models in the at least one nontransitory processor-readable storage medium of the machine learning system. receives learning data comprising a plurality of batches of unlabelled image sets, wherein each image set comprises a source image and target image that each represents a medical image scan of at least one patient; trains one or more convolutional neural networks (CNNs) models, based on the learning data, to learn one or more transformation functions for coregistration of a target image onto a source image, wherein the one or more CNNs models include a global network component that learns an affine transformation matrix and a local network component that learns a dense deformation field, and wherein the global network component precedes the local network component in the one or more CNNs models; and wherein the one or more CNNs models include a global network component that learns an affine transformation matrix and a local network component that learns a dense deformation field, and wherein the global network component precedes the local network component in the one or more CNNs models; and stores the one or more trained CNN models in the at least one nontransitory processor-readable storage medium of the machine learning system. <EOS>